scLANE Simulation Study - Simulated Data Quality Control
UF Dept. of Biostatistics - Bacher Group
Jack Leary
2023-01-14
Libraries
First we’ll load in the packages we need to tidy & analyze our simulation results.
library(dplyr) # data manipulation
library(scran) # scRNA tools
library(scater) # more scRNA tools
library(Seurat) # scRNA methods & data structures
library(ggplot2) # plots
library(targets) # pipeline tools
library(paletteer) # plot colors
library(patchwork) # plot combination
library(SingleCellExperiment) # scRNA data structures Lung Reference
First we’ll load in the lung reference dataset from the {scRNAseq} package & process it like we would our simulated datasets. Please ignore the calls to gc() littered throughout the code, it was so difficult to get all these datasets into memory & processed without my R session crashing over & over again.
lung_data <- scRNAseq::ZilionisLungData(which = "human", filter = TRUE)
lung_data_clean <- lung_data[rowSums(counts(lung_data) > 0) >= 3, ] # genes in at least 3 cells
colnames(lung_data_clean) <- make.unique(colnames(lung_data_clean))
gc(full = TRUE)
# process
lung_data_clean <- logNormCounts(lung_data_clean)
var_decomp <- modelGeneVar(lung_data_clean)
top2k_hvgs <- getTopHVGs(var_decomp, n = 2000)
gc(full = TRUE)
lung_data_clean <- runPCA(lung_data_clean, subset_row = top2k_hvgs)
reducedDim(lung_data_clean, "PCAsub") <- reducedDim(lung_data_clean, "PCA")[, 1:30, drop = FALSE]
lung_data_clean <- runUMAP(lung_data_clean,
dimred = "PCAsub",
n_dimred = 1:30)
gc(full = TRUE)
g <- buildSNNGraph(lung_data_clean,
use.dimred = "PCAsub",
k = 30)
clusters <- igraph::cluster_louvain(graph = g)$membership
colLabels(lung_data_clean) <- factor(clusters)
gc(full = TRUE)
lung_data_clean <- as.Seurat(lung_data_clean,
counts = "counts",
data = "logcounts")
gc(full = TRUE)Next we create a table of summary statistics & dataset characteristics. We then create a {ggplot2}-friendly version of the table using {gridExtra} & {gtable}. This allows us to include the table as part of a plot object.
n_cells <- ncol(lung_data_clean)
n_genes <- nrow(lung_data_clean)
mean_count <- mean(lung_data_clean@assays$originalexp@counts)
med_count <- 0
sd_count <- sd(lung_data_clean@assays$originalexp@counts)
var_count <- sd_count^2 # faster
range_count <- range(lung_data_clean@assays$originalexp@counts)
sparsity_count <- mean(lung_data_clean@assays$originalexp@counts == 0)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
med_count,
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
plot_table <- gridExtra::tableGrob(summary_df,
rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
gc(full = TRUE)The first plot we want is a histogram of raw counts. Unfortunately, this is apparently very hard to do since our data is so large. We first convert our counts matrix to a file-backed matrix, and manually compute the bin ranges we’re interested in (with \(b = 20\) bins). Next, we iterate over the bins & sum the number of counts within each range. However, this too cause issues, so we also iterate over the columns of the matrix (cells), and sum within each cell first, then aggregate afterwards for the final per-bin value.
bin_df <- data.frame(lower_bound = seq(range_count[1], range_count[2], length.out = 20)) %>%
mutate(upper_bound = lead(lower_bound),
across(contains("bound"), \(x) round(x))) %>%
filter(!is.na(upper_bound)) %>%
mutate(n = NA_real_)
counts_mat <- bigstatsr::as_FBM(as.matrix(lung_data_clean@assays$originalexp@counts),
type = "integer",
is_read_only = TRUE)
gc(full = TRUE)
for (b in seq(nrow(bin_df))) {
bin_sums_tmp <- vector("numeric", length = ncol(counts_mat))
for (j in seq(ncol(counts_mat))) {
col_j <- counts_mat[, j]
if (b == 1) {
bin_sums_tmp[j] <- sum(col_j >= bin_df$lower_bound[b] & col_j <= bin_df$upper_bound[b])
} else {
bin_sums_tmp[j] <- sum(col_j > bin_df$lower_bound[b] & col_j <= bin_df$upper_bound[b])
}
rm(col_j)
}
bin_sum <- sum(bin_sums_tmp)
bin_df$n[b] <- bin_sum
gc(full = TRUE)
}
rm(counts_mat); gc(full = TRUE)
p0 <- ggplot(bin_df, aes(x = lower_bound, y = n)) +
geom_bar(stat = "identity", fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)We’ll do the same for the normalized counts.
bin_df <- data.frame(lower_bound = seq(0, max(lung_data_clean@assays$originalexp@data), length.out = 20)) %>%
mutate(upper_bound = lead(lower_bound)) %>%
filter(!is.na(upper_bound)) %>%
mutate(n = NA_real_)
counts_mat <- bigstatsr::as_FBM(as.matrix(lung_data_clean@assays$originalexp@data),
type = "float",
is_read_only = TRUE)
gc(full = TRUE)
for (b in seq(nrow(bin_df))) {
bin_sums_tmp <- vector("numeric", length = ncol(counts_mat))
for (j in seq(ncol(counts_mat))) {
col_j <- counts_mat[, j]
if (b == 1) {
bin_sums_tmp[j] <- sum(col_j >= bin_df$lower_bound[b] & col_j <= bin_df$upper_bound[b])
} else {
bin_sums_tmp[j] <- sum(col_j > bin_df$lower_bound[b] & col_j <= bin_df$upper_bound[b])
}
rm(col_j)
}
bin_sum <- sum(bin_sums_tmp)
bin_df$n[b] <- bin_sum
gc(full = TRUE)
}
rm(counts_mat); gc(full = TRUE)
p1 <- ggplot(bin_df, aes(x = lower_bound, y = n)) +
geom_bar(stat = "identity", fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)Now that the tricky stuff is out of the way, we make a UMAP & PCA plot of the unsupervised clustering.
p2 <- data.frame(UMAP1 = lung_data_clean@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = lung_data_clean@reductions$UMAP@cell.embeddings[, 2],
cluster = lung_data_clean$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
guides(color = guide_legend(override.aes = list(size = 2)))
p3 <- data.frame(PC1 = lung_data_clean@reductions$PCA@cell.embeddings[, 1],
PC2 = lung_data_clean@reductions$PCA@cell.embeddings[, 2],
cluster = lung_data_clean$label) %>%
ggplot(aes(x = PC1, y = PC2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "PC 1", y = "PC 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(legend.position = "none",
axis.text = element_blank(),
axis.ticks = element_blank())We align everything using the {patchwork} package, save the figure, and plot it all.
p4a <- (p0 | p1) / (p2 | p3) +
plot_layout(guides = "collect")
p4b <- (p4a | plot_table) +
plot_layout(ncol = 2, widths = c(3, 1)) +
plot_annotation(title = paste0("Metrics for Lung Reference Dataset"))
ggsave(filename = "QC_lung_reference.pdf",
plot = p4b,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 8,
units = "in",
dpi = "retina")
p4b
Single-subject
We load the single-subject simulated datasets into a list & turn them into {Seurat} objects for plotting.
# 100 cells
tar_load(lung_sim_DEG_01_CELLS_100)
tar_load(lung_sim_DEG_05_CELLS_100)
tar_load(lung_sim_DEG_10_CELLS_100)
tar_load(lung_sim_DEG_20_CELLS_100)
# 500 cells
tar_load(lung_sim_DEG_01_CELLS_500)
tar_load(lung_sim_DEG_05_CELLS_500)
tar_load(lung_sim_DEG_10_CELLS_500)
tar_load(lung_sim_DEG_20_CELLS_500)
# 1,000 cells
tar_load(lung_sim_DEG_01_CELLS_1000)
tar_load(lung_sim_DEG_05_CELLS_1000)
tar_load(lung_sim_DEG_10_CELLS_1000)
tar_load(lung_sim_DEG_20_CELLS_1000)
# 2,500 cells
tar_load(lung_sim_DEG_01_CELLS_2500)
tar_load(lung_sim_DEG_05_CELLS_2500)
tar_load(lung_sim_DEG_10_CELLS_2500)
tar_load(lung_sim_DEG_20_CELLS_2500)
# 5,000 cells
tar_load(lung_sim_DEG_01_CELLS_5000)
tar_load(lung_sim_DEG_05_CELLS_5000)
tar_load(lung_sim_DEG_10_CELLS_5000)
tar_load(lung_sim_DEG_20_CELLS_5000)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "lung_sim")[!grepl("balanced", ls(pattern = "lung_sim"))], function(sim) {
obj <- eval(as.symbol(sim))
obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
obj@meta.data <- mutate(obj@meta.data,
perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "lung_sim_DEG_.."), "lung_sim_DEG_")), "%"),
n_cells = as.character(ncol(obj)),
n_genes = as.character(nrow(obj)),
sce_name = sim)
return(obj)
})
rm(list = ls(pattern = "lung_sim")); gc(full = TRUE)Iterating over the datasets, we print each QC plot & save them to PDFs.
purrr::walk(obj_list, function(z) {
# gather metadata
obj_name <- z@meta.data$sce_name[1]
n_cells <- z@meta.data$n_cells[1]
n_genes <- z@meta.data$n_genes[1]
perc_deg <- z@meta.data$perc_deg[1]
# summary stat table
sparsity_count <- mean(z@assays$originalexp@counts == 0)
mean_count <- mean(z@assays$originalexp@counts)
med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
sd_count <- sd(z@assays$originalexp@counts)
var_count <- sd_count^2
range_count <- range(z@assays$originalexp@counts)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
round(med_count, 2),
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
# create counts histogram
p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create log counts histogram
p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create UMAP by cluster
p2 <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
guides(color = guide_legend(override.aes = list(size = 2)))
# create PCA of cell ordering
p3 <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
# table of simulation parameters
param_df <- data.frame(metric = c("Number of Cells", "Number of Genes", "% Dynamic Genes"),
value = c(as.character(n_cells), as.character(n_genes), perc_deg))
plot_table <- rbind(summary_df, param_df) %>%
gridExtra::tableGrob(rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
# align everything
p4a <- (p0 | p1) / (p2 | p3) +
plot_layout(guides = "collect")
p4b <- (p4a | plot_table) +
plot_layout(ncol = 2, widths = c(3, 1)) +
plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
# save & print plot
ggsave(filename = paste0("QC_", obj_name, ".pdf"),
plot = p4b,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 8,
units = "in",
dpi = "retina")
print(p4b)
# cleanup
sink(tempfile())
rm(p0, p1, p2, p3, p4a, p4b, plot_table); gc(full = TRUE)
sink()
})
rm(obj_list)Multi-subject
We repeat the process for the multi-subject simulated datasets.
# 100 cells
tar_load(lung_sim_DEG_10_CELLS_100_balanced)
tar_load(lung_sim_DEG_20_CELLS_100_balanced)
tar_load(lung_sim_DEG_10_CELLS_100_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_100_unbalanced)
# 500 cells
tar_load(lung_sim_DEG_10_CELLS_500_balanced)
tar_load(lung_sim_DEG_20_CELLS_500_balanced)
tar_load(lung_sim_DEG_10_CELLS_500_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_500_unbalanced)
# 1,000 cells
tar_load(lung_sim_DEG_10_CELLS_1000_balanced)
tar_load(lung_sim_DEG_20_CELLS_1000_balanced)
tar_load(lung_sim_DEG_10_CELLS_1000_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_1000_unbalanced)
# 2,500 cells
tar_load(lung_sim_DEG_10_CELLS_2500_balanced)
tar_load(lung_sim_DEG_20_CELLS_2500_balanced)
tar_load(lung_sim_DEG_10_CELLS_2500_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_2500_unbalanced)
# 5,000 cells
tar_load(lung_sim_DEG_10_CELLS_5000_balanced)
tar_load(lung_sim_DEG_20_CELLS_5000_balanced)
tar_load(lung_sim_DEG_10_CELLS_5000_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_5000_unbalanced)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "lung_sim*balanced"), function(sim) {
obj <- eval(as.symbol(sim))
obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
obj@meta.data <- mutate(obj@meta.data,
perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "lung_sim_DEG_.."), "lung_sim_DEG_")), "%"),
n_cells = as.character(ncol(obj)),
n_genes = as.character(nrow(obj)),
allocation = ifelse(grepl("_balanced", sim), "Balanced", "Unbalanced"),
sce_name = sim)
return(obj)
})
rm(list = ls(pattern = "lung_sim")); gc(full = TRUE)We add a UMAP of the subject identities, & a PCA plot of the true pseudotime split by subject when generating the QC plots for multi-subject data.
purrr::walk(obj_list, function(z) {
# gather metadata
obj_name <- z@meta.data$sce_name[1]
n_cells <- z@meta.data$n_cells[1]
n_genes <- z@meta.data$n_genes[1]
perc_deg <- z@meta.data$perc_deg[1]
allocation <- z@meta.data$allocation[1]
n_subjects <- length(unique(z@meta.data$subject))
# summary stat table
sparsity_count <- mean(z@assays$originalexp@counts == 0)
mean_count <- mean(z@assays$originalexp@counts)
med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
sd_count <- sd(z@assays$originalexp@counts)
var_count <- sd_count^2
range_count <- range(z@assays$originalexp@counts)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
round(med_count, 2),
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
# create counts histogram
p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create log counts histogram
p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create UMAP by cluster & subject
legend_clust <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")[1:length(unique(z$label))]) +
labs(color = "Louvain\nCluster") +
theme_classic(base_size = 14) + theme(legend.text.align = 0.5)))
legend_subj <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
subject = z$subject) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = subject)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")[(length(unique(z$label)) + 1):20]) +
labs(color = "Subject") +
theme_classic(base_size = 14)))
p2a <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label,
subject = z$subject) %>%
tidyr::pivot_longer(cols = c(cluster, subject), names_to = "ident", values_to = "ident_value") %>%
mutate(ident = case_when(ident == "cluster" ~ "Louvain Cluster",
TRUE ~ "Subject")) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = ident_value, group = ident)) +
facet_wrap(~ident) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme_classic(base_size = 14) +
theme(legend.position = "none",
axis.text = element_blank(),
axis.ticks = element_blank())
p2b <- (p2a + (wrap_elements(legend_clust) / wrap_elements(legend_subj)) +
plot_layout(ncol = 2, widths = c(4, 1)))
# create PCA of cell ordering
p3a <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed,
subject = z$subject) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
facet_wrap(~subject) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p3b <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p3c <- (p3a | p3b) +
plot_layout(guides = "collect", widths = c(3, 2), ncol = 2)
# table of simulation parameters
param_df <- data.frame(metric = c("N Cells", "N Genes", "% Dynamic", "Allocation", "N Subjects"),
value = c(as.character(n_cells),
as.character(n_genes),
perc_deg,
allocation,
n_subjects))
plot_table <- rbind(summary_df, param_df) %>%
gridExtra::tableGrob(rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
# align everything
p4a <- ((p0 | p1 | plot_table) + plot_layout(widths = c(1, 1, 0.5))) / (p2b | p3c) +
plot_layout(nrow = 2) +
plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
# save & print plot
ggsave(filename = paste0("QC_", obj_name, ".pdf"),
plot = p4a,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 9,
units = "in",
dpi = "retina")
print(p4a)
# cleanup
sink(tempfile())
rm(p0, p1, p2, p3, p4a, plot_table); gc(full = TRUE)
sink()
})
rm(obj_list)Pancreas Reference
Next let’s examine the pancreas reference dataset. Since it’s smaller than the lung reference, we don’t need to resort to trickery to generate our histograms.
panc_data <- scRNAseq::BaronPancreasData(which = "human")
panc_data_clean <- panc_data[rowSums(counts(panc_data) > 0) >= 3, ] # genes in at least 3 cells
panc_data_clean <- logNormCounts(panc_data_clean)
var_decomp <- modelGeneVar(panc_data_clean)
top2k_hvgs <- getTopHVGs(var_decomp, n = 2000)
panc_data_clean <- runPCA(panc_data_clean, subset_row = top2k_hvgs)
reducedDim(panc_data_clean, "PCAsub") <- reducedDim(panc_data_clean, "PCA")[, 1:30, drop = FALSE]
panc_data_clean <- runUMAP(panc_data_clean,
dimred = "PCAsub",
n_dimred = 1:30)
g <- buildSNNGraph(panc_data_clean,
use.dimred = "PCAsub",
k = 30)
clusters <- igraph::cluster_louvain(graph = g)$membership
colLabels(panc_data_clean) <- factor(clusters)
panc_data_clean <- as.Seurat(panc_data_clean,
counts = "counts",
data = "logcounts")We re-create the summary statistics table for the pancreas reference.
n_cells <- ncol(panc_data_clean)
n_genes <- nrow(panc_data_clean)
mean_count <- mean(panc_data_clean@assays$originalexp@counts)
med_count <- 0
sd_count <- sd(panc_data_clean@assays$originalexp@counts)
var_count <- sd_count^2 # faster
range_count <- range(panc_data_clean@assays$originalexp@counts)
sparsity_count <- mean(panc_data_clean@assays$originalexp@counts == 0)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
med_count,
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
plot_table <- gridExtra::tableGrob(summary_df, rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
gc(full = TRUE)We create the two histograms in the usual way.
p0 <- data.frame(x = as.numeric(panc_data_clean@assays$originalexp@counts)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)
p1 <- data.frame(x = as.numeric(panc_data_clean@assays$originalexp@data)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)The UMAP & PCA plots are generated as before.
p2 <- data.frame(UMAP1 = panc_data_clean@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = panc_data_clean@reductions$UMAP@cell.embeddings[, 2],
cluster = panc_data_clean$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
guides(color = guide_legend(override.aes = list(size = 2)))
p3 <- data.frame(PC1 = panc_data_clean@reductions$PCA@cell.embeddings[, 1],
PC2 = panc_data_clean@reductions$PCA@cell.embeddings[, 2],
cluster = panc_data_clean$label) %>%
ggplot(aes(x = PC1, y = PC2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "PC 1", y = "PC 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(legend.position = "none",
axis.text = element_blank(),
axis.ticks = element_blank())Lastly, we align everything & plot it.
p4a <- (p0 | p1) / (p2 | p3) +
plot_layout(guides = "collect")
p4b <- (p4a | plot_table) +
plot_layout(ncol = 2, widths = c(3, 1)) +
plot_annotation(title = paste0("Metrics for Pancreas Reference Dataset"))
ggsave(filename = "QC_panc_reference.pdf",
plot = p4b,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 8,
units = "in",
dpi = "retina")
p4b
Single-subject
Here we bring the single subject simulations from the pancreas reference into memory.
# 100 cells
tar_load(panc_sim_DEG_01_CELLS_100)
tar_load(panc_sim_DEG_05_CELLS_100)
tar_load(panc_sim_DEG_10_CELLS_100)
tar_load(panc_sim_DEG_20_CELLS_100)
# 500 cells
tar_load(panc_sim_DEG_01_CELLS_500)
tar_load(panc_sim_DEG_05_CELLS_500)
tar_load(panc_sim_DEG_10_CELLS_500)
tar_load(panc_sim_DEG_20_CELLS_500)
# 1,000 cells
tar_load(panc_sim_DEG_01_CELLS_1000)
tar_load(panc_sim_DEG_05_CELLS_1000)
tar_load(panc_sim_DEG_10_CELLS_1000)
tar_load(panc_sim_DEG_20_CELLS_1000)
# 2,500 cells
tar_load(panc_sim_DEG_01_CELLS_2500)
tar_load(panc_sim_DEG_05_CELLS_2500)
tar_load(panc_sim_DEG_10_CELLS_2500)
tar_load(panc_sim_DEG_20_CELLS_2500)
# 5,000 cells
tar_load(panc_sim_DEG_01_CELLS_5000)
tar_load(panc_sim_DEG_05_CELLS_5000)
tar_load(panc_sim_DEG_10_CELLS_5000)
tar_load(panc_sim_DEG_20_CELLS_5000)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "panc_sim")[!grepl("balanced", ls(pattern = "panc_sim"))], function(sim) {
obj <- eval(as.symbol(sim))
obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
obj@meta.data <- mutate(obj@meta.data,
perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "panc_sim_DEG_.."), "panc_sim_DEG_")), "%"),
n_cells = as.character(ncol(obj)),
n_genes = as.character(nrow(obj)),
sce_name = sim)
return(obj)
})
rm(list = ls(pattern = "panc_sim")); gc(full = TRUE)Let’s generate the plots for each dataset.
purrr::walk(obj_list, function(z) {
# gather metadata
obj_name <- z@meta.data$sce_name[1]
n_cells <- z@meta.data$n_cells[1]
n_genes <- z@meta.data$n_genes[1]
perc_deg <- z@meta.data$perc_deg[1]
# summary stat table
sparsity_count <- mean(z@assays$originalexp@counts == 0)
mean_count <- mean(z@assays$originalexp@counts)
med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
sd_count <- sd(z@assays$originalexp@counts)
var_count <- sd_count^2
range_count <- range(z@assays$originalexp@counts)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
round(med_count, 2),
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
# create counts histogram
p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create log counts histogram
p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create UMAP by cluster
p2 <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
guides(color = guide_legend(override.aes = list(size = 2)))
# create PCA of cell ordering
p3 <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
# table of simulation parameters
param_df <- data.frame(metric = c("Number of Cells", "Number of Genes", "% Dynamic Genes"),
value = c(as.character(n_cells), as.character(n_genes), perc_deg))
plot_table <- rbind(summary_df, param_df) %>%
gridExtra::tableGrob(rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
# align everything
p4a <- (p0 | p1) / (p2 | p3) +
plot_layout(guides = "collect")
p4b <- (p4a | plot_table) +
plot_layout(ncol = 2, widths = c(3, 1)) +
plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
# save & print plot
ggsave(filename = paste0("QC_", obj_name, ".pdf"),
plot = p4b,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 8,
units = "in",
dpi = "retina")
print(p4b)
# cleanup
sink(tempfile())
rm(p0, p1, p2, p3, p4a, p4b, plot_table); gc(full = TRUE)
sink()
})
rm(obj_list)Multi-subject
We load in the multi-subject simulated datasets from the pancreas reference.
# 100 cells
tar_load(panc_sim_DEG_10_CELLS_100_balanced)
tar_load(panc_sim_DEG_20_CELLS_100_balanced)
tar_load(panc_sim_DEG_10_CELLS_100_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_100_unbalanced)
# 500 cells
tar_load(panc_sim_DEG_10_CELLS_500_balanced)
tar_load(panc_sim_DEG_20_CELLS_500_balanced)
tar_load(panc_sim_DEG_10_CELLS_500_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_500_unbalanced)
# 1,000 cells
tar_load(panc_sim_DEG_10_CELLS_1000_balanced)
tar_load(panc_sim_DEG_20_CELLS_1000_balanced)
tar_load(panc_sim_DEG_10_CELLS_1000_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_1000_unbalanced)
# 2,500 cells
tar_load(panc_sim_DEG_10_CELLS_2500_balanced)
tar_load(panc_sim_DEG_20_CELLS_2500_balanced)
tar_load(panc_sim_DEG_10_CELLS_2500_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_2500_unbalanced)
# 5,000 cells
tar_load(panc_sim_DEG_10_CELLS_5000_balanced)
tar_load(panc_sim_DEG_20_CELLS_5000_balanced)
tar_load(panc_sim_DEG_10_CELLS_5000_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_5000_unbalanced)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "panc_sim_*balanced"), function(sim) {
obj <- eval(as.symbol(sim))
obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
obj@meta.data <- mutate(obj@meta.data,
perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "panc_sim_DEG_.."), "panc_sim_DEG_")), "%"),
n_cells = as.character(ncol(obj)),
n_genes = as.character(nrow(obj)),
allocation = ifelse(grepl("_balanced", sim), "Balanced", "Unbalanced"),
sce_name = sim)
return(obj)
})
rm(list = ls(pattern = "panc_sim")); gc(full = TRUE)We regenerate the slightly more complex multi-subject figures as we did for the lung dataset.
purrr::walk(obj_list, function(z) {
# gather metadata
obj_name <- z@meta.data$sce_name[1]
n_cells <- z@meta.data$n_cells[1]
n_genes <- z@meta.data$n_genes[1]
perc_deg <- z@meta.data$perc_deg[1]
allocation <- z@meta.data$allocation[1]
n_subjects <- length(unique(z@meta.data$subject))
# summary stat table
sparsity_count <- mean(z@assays$originalexp@counts == 0)
mean_count <- mean(z@assays$originalexp@counts)
med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
sd_count <- sd(z@assays$originalexp@counts)
var_count <- sd_count^2
range_count <- range(z@assays$originalexp@counts)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
round(med_count, 2),
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
# create counts histogram
p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create log counts histogram
p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create UMAP by cluster & subject
legend_clust <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")[1:length(unique(z$label))]) +
labs(color = "Louvain\nCluster") +
theme_classic(base_size = 14) + theme(legend.text.align = 0.5)))
legend_subj <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
subject = z$subject) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = subject)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")[(length(unique(z$label)) + 1):20]) +
labs(color = "Subject") +
theme_classic(base_size = 14)))
p2a <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label,
subject = z$subject) %>%
tidyr::pivot_longer(cols = c(cluster, subject), names_to = "ident", values_to = "ident_value") %>%
mutate(ident = case_when(ident == "cluster" ~ "Louvain Cluster",
TRUE ~ "Subject")) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = ident_value, group = ident)) +
facet_wrap(~ident) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme_classic(base_size = 14) +
theme(legend.position = "none",
axis.text = element_blank(),
axis.ticks = element_blank())
p2b <- (p2a + (wrap_elements(legend_clust) / wrap_elements(legend_subj)) +
plot_layout(ncol = 2, widths = c(4, 1)))
# create PCA of cell ordering
p3a <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed,
subject = z$subject) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
facet_wrap(~subject) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p3b <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p3c <- (p3a | p3b) +
plot_layout(guides = "collect", widths = c(3, 2), ncol = 2)
# table of simulation parameters
param_df <- data.frame(metric = c("N Cells", "N Genes", "% Dynamic", "Allocation", "N Subjects"),
value = c(as.character(n_cells),
as.character(n_genes),
perc_deg,
allocation,
n_subjects))
plot_table <- rbind(summary_df, param_df) %>%
gridExtra::tableGrob(rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
# align everything
p4a <- ((p0 | p1 | plot_table) + plot_layout(widths = c(1, 1, 0.5))) / (p2b | p3c) +
plot_layout(nrow = 2) +
plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
# save & print plot
ggsave(filename = paste0("QC_", obj_name, ".pdf"),
plot = p4b,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 9,
units = "in",
dpi = "retina")
print(p4a)
# cleanup
sink(tempfile())
rm(p0, p1, p2a, p2b, p3a, p3b, p3c, p4a, plot_table); gc(full = TRUE)
sink()
})
rm(obj_list)Pancreatic Endocrinogenesis Reference
First let’s take a look at the reference dataset itself. Loading this one is a bit more complicated - it comes as part of the scVelo Python library, so we use the {reticulate} R package to access & load it before converting it to a SingleCellExperiment object using the {zellkonverter} package. Though the data comes pre-processed, we reprocess it using the same steps as we did on the other two datasets to ensure a good comparison. A necessary wrinkle here is the activation of a conda environment that I created previously & installed scVelo and its dependencies into.
reticulate::use_condaenv(condaenv = "HPG_venv",
conda = "/apps/conda/22.11.1/condabin/conda",
required = TRUE)
scvelo <- reticulate::import("scvelo")
adata <- scvelo$datasets$pancreas()
endo <- zellkonverter::AnnData2SCE(adata = adata)
endo@assays@data$X <- NULL
endo@assays@data$counts <- endo@assays@data$spliced
endo_data_clean <- endo[rowSums(SingleCellExperiment::counts(endo) > 0) >= 3, ]
endo_data_clean <- logNormCounts(endo_data_clean)
var_decomp <- modelGeneVar(endo_data_clean)
top2k_hvgs <- getTopHVGs(var_decomp, n = 2000)
endo_data_clean <- runPCA(endo_data_clean, subset_row = top2k_hvgs)
reducedDim(endo_data_clean, "PCAsub") <- reducedDim(endo_data_clean, "PCA")[, 1:30, drop = FALSE]
endo_data_clean <- runUMAP(endo_data_clean,
dimred = "PCAsub",
n_dimred = 1:30)
g <- buildSNNGraph(endo_data_clean,
use.dimred = "PCAsub",
k = 30)
clusters <- igraph::cluster_louvain(graph = g)$membership
colLabels(endo_data_clean) <- factor(clusters)
endo_data_clean <- as.Seurat(endo_data_clean,
counts = "counts",
data = "logcounts")We re-create the summary statistics table for the pancreas reference.
n_cells <- ncol(endo_data_clean)
n_genes <- nrow(endo_data_clean)
mean_count <- mean(endo_data_clean@assays$originalexp@counts)
med_count <- 0
sd_count <- sd(endo_data_clean@assays$originalexp@counts)
var_count <- sd_count^2 # faster
range_count <- range(endo_data_clean@assays$originalexp@counts)
sparsity_count <- mean(endo_data_clean@assays$originalexp@counts == 0)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
med_count,
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
plot_table <- gridExtra::tableGrob(summary_df, rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
gc(full = TRUE)Like the pancreas reference, this dataset is small enough that we don’t need to do any trickery to produce histograms.
p0 <- data.frame(x = as.numeric(endo_data_clean@assays$originalexp@counts)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)
p1 <- data.frame(x = as.numeric(endo_data_clean@assays$originalexp@data)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)The UMAP & PCA plots are generated as before.
p2 <- data.frame(UMAP1 = endo_data_clean@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = endo_data_clean@reductions$UMAP@cell.embeddings[, 2],
cluster = endo_data_clean$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
guides(color = guide_legend(override.aes = list(size = 2)))
p3 <- data.frame(PC1 = endo_data_clean@reductions$PCA@cell.embeddings[, 1],
PC2 = endo_data_clean@reductions$PCA@cell.embeddings[, 2],
cluster = endo_data_clean$label) %>%
ggplot(aes(x = PC1, y = PC2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "PC 1", y = "PC 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(legend.position = "none",
axis.text = element_blank(),
axis.ticks = element_blank())
p3
We save the final plot as a PDF & display it.
p4a <- (p0 | p1) / (p2 | p3) +
plot_layout(guides = "collect")
p4b <- (p4a | plot_table) +
plot_layout(ncol = 2, widths = c(3, 1)) +
plot_annotation(title = paste0("Metrics for Endocrinogenesis Reference Dataset"))
ggsave(filename = "QC_endo_reference.pdf",
plot = p4b,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 8,
units = "in",
dpi = "retina")
p4b
Single-subject
We load the single-subject endocrinogenesis datasets.
# 100 cells
tar_load(endo_sim_DEG_01_CELLS_100)
tar_load(endo_sim_DEG_05_CELLS_100)
tar_load(endo_sim_DEG_10_CELLS_100)
tar_load(endo_sim_DEG_20_CELLS_100)
# 500 cells
tar_load(endo_sim_DEG_01_CELLS_500)
tar_load(endo_sim_DEG_05_CELLS_500)
tar_load(endo_sim_DEG_10_CELLS_500)
tar_load(endo_sim_DEG_20_CELLS_500)
# 1,000 cells
tar_load(endo_sim_DEG_01_CELLS_1000)
tar_load(endo_sim_DEG_05_CELLS_1000)
tar_load(endo_sim_DEG_10_CELLS_1000)
tar_load(endo_sim_DEG_20_CELLS_1000)
# 2,500 cells
tar_load(endo_sim_DEG_01_CELLS_2500)
tar_load(endo_sim_DEG_05_CELLS_2500)
tar_load(endo_sim_DEG_10_CELLS_2500)
tar_load(endo_sim_DEG_20_CELLS_2500)
# 5,000 cells
tar_load(endo_sim_DEG_01_CELLS_5000)
tar_load(endo_sim_DEG_05_CELLS_5000)
tar_load(endo_sim_DEG_10_CELLS_5000)
tar_load(endo_sim_DEG_20_CELLS_5000)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "endo_sim")[!grepl("balanced", ls(pattern = "endo_sim"))], function(sim) {
obj <- eval(as.symbol(sim))
obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
obj@meta.data <- mutate(obj@meta.data,
perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "endo_sim_DEG_.."), "endo_sim_DEG_")), "%"),
n_cells = as.character(ncol(obj)),
n_genes = as.character(nrow(obj)),
sce_name = sim)
return(obj)
})
rm(list = ls(pattern = "endo_sim")); gc(full = TRUE)We generate the plots for each dataset.
purrr::walk(obj_list, function(z) {
# gather metadata
obj_name <- z@meta.data$sce_name[1]
n_cells <- z@meta.data$n_cells[1]
n_genes <- z@meta.data$n_genes[1]
perc_deg <- z@meta.data$perc_deg[1]
# summary stat table
sparsity_count <- mean(z@assays$originalexp@counts == 0)
mean_count <- mean(z@assays$originalexp@counts)
med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
sd_count <- sd(z@assays$originalexp@counts)
var_count <- sd_count^2
range_count <- range(z@assays$originalexp@counts)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
round(med_count, 2),
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
# create counts histogram
p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create log counts histogram
p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create UMAP by cluster
p2 <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
guides(color = guide_legend(override.aes = list(size = 2)))
# create PCA of cell ordering
p3 <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
# table of simulation parameters
param_df <- data.frame(metric = c("Number of Cells", "Number of Genes", "% Dynamic Genes"),
value = c(as.character(n_cells), as.character(n_genes), perc_deg))
plot_table <- rbind(summary_df, param_df) %>%
gridExtra::tableGrob(rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
# align everything
p4a <- (p0 | p1) / (p2 | p3) +
plot_layout(guides = "collect")
p4b <- (p4a | plot_table) +
plot_layout(ncol = 2, widths = c(3, 1)) +
plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
# save & print plot
ggsave(filename = paste0("QC_", obj_name, ".pdf"),
plot = p4b,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 8,
units = "in",
dpi = "retina")
print(p4b)
# cleanup
sink(tempfile())
rm(p0, p1, p2, p3, p4a, p4b, plot_table); gc(full = TRUE)
sink()
})
rm(obj_list)Multi-subject
Lastly, we bring the multi-subject simulated datasets into memory.
# 100 cells
tar_load(endo_sim_DEG_10_CELLS_100_balanced)
tar_load(endo_sim_DEG_20_CELLS_100_balanced)
tar_load(endo_sim_DEG_10_CELLS_100_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_100_unbalanced)
# 500 cells
tar_load(endo_sim_DEG_10_CELLS_500_balanced)
tar_load(endo_sim_DEG_20_CELLS_500_balanced)
tar_load(endo_sim_DEG_10_CELLS_500_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_500_unbalanced)
# 1,000 cells
tar_load(endo_sim_DEG_10_CELLS_1000_balanced)
tar_load(endo_sim_DEG_20_CELLS_1000_balanced)
tar_load(endo_sim_DEG_10_CELLS_1000_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_1000_unbalanced)
# 2,500 cells
tar_load(endo_sim_DEG_10_CELLS_2500_balanced)
tar_load(endo_sim_DEG_20_CELLS_2500_balanced)
tar_load(endo_sim_DEG_10_CELLS_2500_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_2500_unbalanced)
# 5,000 cells
tar_load(endo_sim_DEG_10_CELLS_5000_balanced)
tar_load(endo_sim_DEG_20_CELLS_5000_balanced)
tar_load(endo_sim_DEG_10_CELLS_5000_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_5000_unbalanced)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "endo_sim*balanced"), function(sim) {
obj <- eval(as.symbol(sim))
obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
obj@meta.data <- mutate(obj@meta.data,
perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "endo_sim_DEG_.."), "endo_sim_DEG_")), "%"),
n_cells = as.character(ncol(obj)),
n_genes = as.character(nrow(obj)),
allocation = ifelse(grepl("_balanced", sim), "Balanced", "Unbalanced"),
sce_name = sim)
return(obj)
})
rm(list = ls(pattern = "endo_sim")); gc(full = TRUE)We generate the final set of plots.
purrr::walk(obj_list, function(z) {
# gather metadata
obj_name <- z@meta.data$sce_name[1]
n_cells <- z@meta.data$n_cells[1]
n_genes <- z@meta.data$n_genes[1]
perc_deg <- z@meta.data$perc_deg[1]
allocation <- z@meta.data$allocation[1]
n_subjects <- length(unique(z@meta.data$subject))
# summary stat table
sparsity_count <- mean(z@assays$originalexp@counts == 0)
mean_count <- mean(z@assays$originalexp@counts)
med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
sd_count <- sd(z@assays$originalexp@counts)
var_count <- sd_count^2
range_count <- range(z@assays$originalexp@counts)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"),
value = c(round(mean_count, 2),
round(med_count, 2),
round(sd_count, 2),
round(var_count, 2),
paste0("(", range_count[1], ", ", range_count[2], ")"),
paste0(round(sparsity_count, 4) * 100, "%")))
# create counts histogram
p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "dodgerblue") +
scale_y_continuous(labels = scales::label_scientific()) +
scale_x_continuous(labels = scales::label_comma()) +
labs(x = "Raw Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create log counts histogram
p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>%
ggplot(aes(x = x)) +
geom_histogram(fill = "forestgreen") +
scale_y_continuous(labels = scales::label_scientific()) +
labs(x = "Normalized Expression", y = "Frequency") +
theme_classic(base_size = 14)
# create UMAP by cluster & subject
legend_clust <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")[1:length(unique(z$label))]) +
labs(color = "Louvain\nCluster") +
theme_classic(base_size = 14) + theme(legend.text.align = 0.5)))
legend_subj <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
subject = z$subject) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = subject)) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")[(length(unique(z$label)) + 1):20]) +
labs(color = "Subject") +
theme_classic(base_size = 14)))
p2a <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1],
UMAP2 = z@reductions$UMAP@cell.embeddings[, 2],
cluster = z$label,
subject = z$subject) %>%
tidyr::pivot_longer(cols = c(cluster, subject), names_to = "ident", values_to = "ident_value") %>%
mutate(ident = case_when(ident == "cluster" ~ "Louvain Cluster",
TRUE ~ "Subject")) %>%
ggplot(aes(x = UMAP1, y = UMAP2, color = ident_value, group = ident)) +
facet_wrap(~ident) +
geom_point() +
scale_color_manual(values = paletteer_d("ggsci::category20_d3")) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme_classic(base_size = 14) +
theme(legend.position = "none",
axis.text = element_blank(),
axis.ticks = element_blank())
p2b <- (p2a + (wrap_elements(legend_clust) / wrap_elements(legend_subj)) +
plot_layout(ncol = 2, widths = c(4, 1)))
# create PCA of cell ordering
p3a <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed,
subject = z$subject) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
facet_wrap(~subject) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p3b <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1],
PC2 = z@reductions$PCA@cell.embeddings[, 2],
cell_time = z$cell_time_normed) %>%
ggplot(aes(x = PC1, y = PC2, color = cell_time)) +
geom_point() +
scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
labs(x = "PC 1", y = "PC 2", color = "True Ordering") +
theme_classic(base_size = 14) +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p3c <- (p3a | p3b) +
plot_layout(guides = "collect", widths = c(3, 2), ncol = 2)
# table of simulation parameters
param_df <- data.frame(metric = c("N Cells", "N Genes", "% Dynamic", "Allocation", "N Subjects"),
value = c(as.character(n_cells),
as.character(n_genes),
perc_deg,
allocation,
n_subjects))
plot_table <- rbind(summary_df, param_df) %>%
gridExtra::tableGrob(rows = NULL,
cols = c("Metric", "Value"),
theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)),
colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 2,
b = nrow(.),
l = 1,
r = ncol(.)) %>%
gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)),
t = 1,
l = 1,
r = ncol(.))
# align everything
p4a <- ((p0 | p1 | plot_table) + plot_layout(widths = c(1, 1, 0.5))) / (p2b | p3c) +
plot_layout(nrow = 2) +
plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
# save & print plot
ggsave(filename = paste0("QC_", obj_name, ".pdf"),
plot = p4b,
device = "pdf",
path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/",
width = 13,
height = 9,
units = "in",
dpi = "retina")
print(p4a)
# cleanup
sink(tempfile())
rm(p0, p1, p2a, p2b, p3a, p3b, p3c, p4a, plot_table); gc(full = TRUE)
sink()
})
rm(obj_list)Session Info
sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.2 Patched (2022-11-10 r83330)
## os Ubuntu 22.04.1 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2023-01-14
## pandoc 2.9.2.1 @ /usr/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [2] CRAN (R 4.2.0)
## AnnotationDbi 1.58.0 2022-04-26 [2] Bioconductor
## AnnotationFilter 1.20.0 2022-04-26 [2] Bioconductor
## AnnotationHub 3.4.0 2022-04-26 [2] Bioconductor
## assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.2.0)
## backports 1.4.1 2021-12-13 [2] CRAN (R 4.2.0)
## base64url 1.4 2018-05-14 [2] CRAN (R 4.2.0)
## basilisk 1.8.1 2022-08-25 [2] Bioconductor
## basilisk.utils 1.8.0 2022-04-26 [2] Bioconductor
## beachmat 2.12.0 2022-04-26 [2] Bioconductor
## beeswarm 0.4.0 2021-06-01 [2] CRAN (R 4.2.0)
## bigassertr 0.1.5 2021-07-08 [2] CRAN (R 4.2.0)
## bigparallelr 0.3.2 2021-10-02 [2] CRAN (R 4.2.0)
## bigstatsr 1.5.6 2022-02-03 [2] CRAN (R 4.2.0)
## Biobase * 2.56.0 2022-04-26 [2] Bioconductor
## BiocFileCache 2.4.0 2022-04-26 [2] Bioconductor
## BiocGenerics * 0.42.0 2022-04-26 [2] Bioconductor
## BiocIO 1.6.0 2022-04-26 [2] Bioconductor
## BiocManager 1.30.18 2022-05-18 [1] CRAN (R 4.2.0)
## BiocNeighbors 1.14.0 2022-04-26 [2] Bioconductor
## BiocParallel 1.30.3 2022-06-05 [2] Bioconductor
## BiocSingular 1.12.0 2022-04-26 [2] Bioconductor
## BiocVersion 3.15.2 2022-03-29 [2] Bioconductor
## biomaRt 2.52.0 2022-04-26 [2] Bioconductor
## Biostrings 2.64.1 2022-08-18 [2] Bioconductor
## bit 4.0.4 2020-08-04 [2] CRAN (R 4.2.0)
## bit64 4.0.5 2020-08-30 [2] CRAN (R 4.2.0)
## bitops 1.0-7 2021-04-24 [2] CRAN (R 4.2.0)
## blob 1.2.3 2022-04-10 [2] CRAN (R 4.2.0)
## bluster 1.6.0 2022-04-26 [2] Bioconductor
## broom 1.0.1 2022-08-29 [2] CRAN (R 4.2.1)
## bslib 0.4.0 2022-07-16 [2] CRAN (R 4.2.0)
## cachem 1.0.6 2021-08-19 [2] CRAN (R 4.2.0)
## callr 3.7.2 2022-08-22 [2] CRAN (R 4.2.1)
## cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.2.0)
## cli 3.4.1 2022-09-23 [2] CRAN (R 4.2.1)
## cluster 2.1.4 2022-08-22 [4] CRAN (R 4.2.1)
## codetools 0.2-18 2020-11-04 [4] CRAN (R 4.2.0)
## colorspace 2.0-3 2022-02-21 [2] CRAN (R 4.2.0)
## cowplot 1.1.1 2020-12-30 [2] CRAN (R 4.2.0)
## crayon 1.5.2 2022-09-29 [2] CRAN (R 4.2.1)
## curl 4.3.2 2021-06-23 [2] CRAN (R 4.2.0)
## data.table 1.14.2 2021-09-27 [2] CRAN (R 4.2.0)
## DBI 1.1.3 2022-06-18 [2] CRAN (R 4.2.0)
## dbplyr 2.2.1 2022-06-27 [2] CRAN (R 4.2.0)
## DelayedArray 0.22.0 2022-04-26 [2] Bioconductor
## DelayedMatrixStats 1.18.1 2022-09-27 [2] Bioconductor
## deldir 1.0-6 2021-10-23 [2] CRAN (R 4.2.0)
## digest 0.6.30 2022-10-18 [2] CRAN (R 4.2.1)
## dir.expiry 1.4.0 2022-04-26 [2] Bioconductor
## doParallel 1.0.17 2022-02-07 [2] CRAN (R 4.2.0)
## dplyr * 1.0.9 2022-04-28 [1] CRAN (R 4.2.0)
## dqrng 0.3.0 2021-05-01 [2] CRAN (R 4.2.0)
## edgeR 3.38.4 2022-08-07 [2] Bioconductor
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##
## [1] /home/j.leary/r_packages_default
## [2] /usr/local/lib/R/site-library
## [3] /usr/lib/R/site-library
## [4] /usr/lib/R/library
##
## ─ Python configuration ───────────────────────────────────────────────────────
## python: /home/j.leary/.conda/envs/HPG_venv/bin/python
## libpython: /home/j.leary/.conda/envs/HPG_venv/lib/libpython3.10.so
## pythonhome: /home/j.leary/.conda/envs/HPG_venv:/home/j.leary/.conda/envs/HPG_venv
## version: 3.10.8 | packaged by conda-forge | (main, Nov 22 2022, 08:26:04) [GCC 10.4.0]
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## numpy_version: 1.23.5
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##
## NOTE: Python version was forced by use_python function
##
## ──────────────────────────────────────────────────────────────────────────────---
title: "`scLANE` Simulation Study - Simulated Data Quality Control"
subtitle: "UF Dept. of Biostatistics - Bacher Group"
author: "Jack Leary" 
date: "`r Sys.Date()`"
output:
  html_document:
    theme: journal
    highlight: tango
    code_folding: show
    code_download: true 
    toc: false
    toc_float: false
    df_print: kable
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE); set.seed(312)  # lucky seed
```

# Libraries 

First we'll load in the packages we need to tidy & analyze our simulation results. 

```{r, results='hide'}
library(dplyr)                 # data manipulation 
library(scran)                 # scRNA tools 
library(scater)                # more scRNA tools
library(Seurat)                # scRNA methods & data structures
library(ggplot2)               # plots
library(targets)               # pipeline tools
library(paletteer)             # plot colors 
library(patchwork)             # plot combination
library(SingleCellExperiment)  # scRNA data structures 
```

# Lung Reference 

First we'll load in the lung reference dataset from the `{scRNAseq}` package & process it like we would our simulated datasets. Please ignore the calls to `gc()` littered throughout the code, it was so difficult to get all these datasets into memory & processed without my R session crashing over & over again. 

```{r, results='hide'}
lung_data <- scRNAseq::ZilionisLungData(which = "human", filter = TRUE)
lung_data_clean <- lung_data[rowSums(counts(lung_data) > 0) >= 3, ]  # genes in at least 3 cells
colnames(lung_data_clean) <- make.unique(colnames(lung_data_clean))
gc(full = TRUE)
# process 
lung_data_clean <- logNormCounts(lung_data_clean)
var_decomp <- modelGeneVar(lung_data_clean)
top2k_hvgs <- getTopHVGs(var_decomp, n = 2000)
gc(full = TRUE)
lung_data_clean <- runPCA(lung_data_clean, subset_row = top2k_hvgs)
reducedDim(lung_data_clean, "PCAsub") <- reducedDim(lung_data_clean, "PCA")[, 1:30, drop = FALSE]
lung_data_clean <- runUMAP(lung_data_clean, 
                           dimred = "PCAsub", 
                           n_dimred = 1:30)
gc(full = TRUE)
g <- buildSNNGraph(lung_data_clean, 
                   use.dimred = "PCAsub", 
                   k = 30)
clusters <- igraph::cluster_louvain(graph = g)$membership
colLabels(lung_data_clean) <- factor(clusters)
gc(full = TRUE)
lung_data_clean <- as.Seurat(lung_data_clean, 
                             counts = "counts", 
                             data = "logcounts")
gc(full = TRUE)
```

Next we create a table of summary statistics & dataset characteristics. We then create a `{ggplot2}`-friendly version of the table using `{gridExtra}` & `{gtable}`. This allows us to include the table as part of a plot object. 

```{r, results='hide'}
n_cells <- ncol(lung_data_clean)
n_genes <- nrow(lung_data_clean)
mean_count <- mean(lung_data_clean@assays$originalexp@counts)
med_count <- 0
sd_count <- sd(lung_data_clean@assays$originalexp@counts)
var_count <- sd_count^2  # faster 
range_count <- range(lung_data_clean@assays$originalexp@counts)
sparsity_count <- mean(lung_data_clean@assays$originalexp@counts == 0)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                         value = c(round(mean_count, 2), 
                                   med_count, 
                                   round(sd_count, 2), 
                                   round(var_count, 2),
                                   paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                   paste0(round(sparsity_count, 4) * 100, "%")))
plot_table <- gridExtra::tableGrob(summary_df, 
                                   rows = NULL, 
                                   cols = c("Metric", "Value"), 
                                   theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                     colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
              gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                      t = 2, 
                                      b = nrow(.), 
                                      l = 1, 
                                      r = ncol(.)) %>% 
              gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                      t = 1, 
                                      l = 1, 
                                      r = ncol(.))
gc(full = TRUE)
```

The first plot we want is a histogram of raw counts. Unfortunately, this is apparently very hard to do since our data is so large. We first convert our counts matrix to a file-backed matrix, and manually compute the bin ranges we're interested in (with $b = 20$ bins). Next, we iterate over the bins & sum the number of counts within each range. However, this too cause issues, so we also iterate over the columns of the matrix (cells), and sum within each cell first, then aggregate afterwards for the final per-bin value. 

```{r, results='hide'}
bin_df <- data.frame(lower_bound = seq(range_count[1], range_count[2], length.out = 20)) %>% 
          mutate(upper_bound = lead(lower_bound), 
                 across(contains("bound"), \(x) round(x))) %>% 
          filter(!is.na(upper_bound)) %>% 
          mutate(n = NA_real_)
counts_mat <- bigstatsr::as_FBM(as.matrix(lung_data_clean@assays$originalexp@counts),
                                type = "integer",
                                is_read_only = TRUE)
gc(full = TRUE)
for (b in seq(nrow(bin_df))) {
  bin_sums_tmp <- vector("numeric", length = ncol(counts_mat))
  for (j in seq(ncol(counts_mat))) {
    col_j <- counts_mat[, j]
    if (b == 1) {
      bin_sums_tmp[j] <- sum(col_j >= bin_df$lower_bound[b] & col_j <= bin_df$upper_bound[b])
    } else {
      bin_sums_tmp[j] <- sum(col_j > bin_df$lower_bound[b] & col_j <= bin_df$upper_bound[b])
    }
    rm(col_j)
  }
  bin_sum <- sum(bin_sums_tmp)
  bin_df$n[b] <- bin_sum
  gc(full = TRUE)
}
rm(counts_mat); gc(full = TRUE)
p0 <- ggplot(bin_df, aes(x = lower_bound, y = n)) + 
      geom_bar(stat = "identity", fill = "dodgerblue") + 
      scale_y_continuous(labels = scales::label_scientific()) + 
      scale_x_continuous(labels = scales::label_comma()) + 
      labs(x = "Raw Expression", y = "Frequency") + 
      theme_classic(base_size = 14)
```

We'll do the same for the normalized counts. 

```{r, results='hide'}
bin_df <- data.frame(lower_bound = seq(0, max(lung_data_clean@assays$originalexp@data), length.out = 20)) %>% 
          mutate(upper_bound = lead(lower_bound)) %>% 
          filter(!is.na(upper_bound)) %>% 
          mutate(n = NA_real_)
counts_mat <- bigstatsr::as_FBM(as.matrix(lung_data_clean@assays$originalexp@data),
                                type = "float",
                                is_read_only = TRUE)
gc(full = TRUE)
for (b in seq(nrow(bin_df))) {
  bin_sums_tmp <- vector("numeric", length = ncol(counts_mat))
  for (j in seq(ncol(counts_mat))) {
    col_j <- counts_mat[, j]
    if (b == 1) {
      bin_sums_tmp[j] <- sum(col_j >= bin_df$lower_bound[b] & col_j <= bin_df$upper_bound[b])
    } else {
      bin_sums_tmp[j] <- sum(col_j > bin_df$lower_bound[b] & col_j <= bin_df$upper_bound[b])
    }
    rm(col_j)
  }
  bin_sum <- sum(bin_sums_tmp)
  bin_df$n[b] <- bin_sum
  gc(full = TRUE)
}
rm(counts_mat); gc(full = TRUE)
p1 <- ggplot(bin_df, aes(x = lower_bound, y = n)) + 
      geom_bar(stat = "identity", fill = "forestgreen") + 
      scale_y_continuous(labels = scales::label_scientific()) + 
      labs(x = "Normalized Expression", y = "Frequency") + 
      theme_classic(base_size = 14)
```

Now that the tricky stuff is out of the way, we make a UMAP & PCA plot of the unsupervised clustering. 

```{r}
p2 <- data.frame(UMAP1 = lung_data_clean@reductions$UMAP@cell.embeddings[, 1], 
                 UMAP2 = lung_data_clean@reductions$UMAP@cell.embeddings[, 2], 
                 cluster = lung_data_clean$label) %>% 
      ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
      geom_point() + 
      scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
      labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") + 
      theme_classic(base_size = 14) + 
      theme(axis.text = element_blank(), 
            axis.ticks = element_blank()) + 
      guides(color = guide_legend(override.aes = list(size = 2)))
p3 <- data.frame(PC1 = lung_data_clean@reductions$PCA@cell.embeddings[, 1], 
                 PC2 = lung_data_clean@reductions$PCA@cell.embeddings[, 2], 
                 cluster = lung_data_clean$label) %>% 
      ggplot(aes(x = PC1, y = PC2, color = cluster)) + 
      geom_point() + 
      scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
      labs(x = "PC 1", y = "PC 2", color = "Louvain Cluster") + 
      theme_classic(base_size = 14) + 
      theme(legend.position = "none", 
            axis.text = element_blank(), 
            axis.ticks = element_blank())
```

We align everything using the `{patchwork}` package, save the figure, and plot it all. 

```{r, fig.width=13, fig.height=8}
p4a <- (p0 | p1) / (p2 | p3) + 
       plot_layout(guides = "collect")
p4b <- (p4a | plot_table) + 
       plot_layout(ncol = 2, widths = c(3, 1)) + 
       plot_annotation(title = paste0("Metrics for Lung Reference Dataset"))
ggsave(filename = "QC_lung_reference.pdf",
       plot = p4b, 
       device = "pdf", 
       path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
       width = 13,
       height = 8, 
       units = "in", 
       dpi = "retina")
p4b
```

## Single-subject

We load the single-subject simulated datasets into a list & turn them into `{Seurat}` objects for plotting. 

```{r, results='hide'}
# 100 cells 
tar_load(lung_sim_DEG_01_CELLS_100)
tar_load(lung_sim_DEG_05_CELLS_100)
tar_load(lung_sim_DEG_10_CELLS_100)
tar_load(lung_sim_DEG_20_CELLS_100)
# 500 cells 
tar_load(lung_sim_DEG_01_CELLS_500)
tar_load(lung_sim_DEG_05_CELLS_500)
tar_load(lung_sim_DEG_10_CELLS_500)
tar_load(lung_sim_DEG_20_CELLS_500)
# 1,000 cells 
tar_load(lung_sim_DEG_01_CELLS_1000)
tar_load(lung_sim_DEG_05_CELLS_1000)
tar_load(lung_sim_DEG_10_CELLS_1000)
tar_load(lung_sim_DEG_20_CELLS_1000)
# 2,500 cells
tar_load(lung_sim_DEG_01_CELLS_2500)
tar_load(lung_sim_DEG_05_CELLS_2500)
tar_load(lung_sim_DEG_10_CELLS_2500)
tar_load(lung_sim_DEG_20_CELLS_2500)
# 5,000 cells 
tar_load(lung_sim_DEG_01_CELLS_5000)
tar_load(lung_sim_DEG_05_CELLS_5000)
tar_load(lung_sim_DEG_10_CELLS_5000)
tar_load(lung_sim_DEG_20_CELLS_5000)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "lung_sim")[!grepl("balanced", ls(pattern = "lung_sim"))], function(sim) {
  obj <- eval(as.symbol(sim))
  obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
  obj@meta.data <- mutate(obj@meta.data, 
                          perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "lung_sim_DEG_.."), "lung_sim_DEG_")), "%"), 
                          n_cells = as.character(ncol(obj)), 
                          n_genes = as.character(nrow(obj)), 
                          sce_name = sim)
  return(obj)
})
rm(list = ls(pattern = "lung_sim")); gc(full = TRUE)
```

Iterating over the datasets, we print each QC plot & save them to PDFs. 

```{r, fig.width=13, fig.height=8, results='hold'}
purrr::walk(obj_list, function(z) {
  # gather metadata 
  obj_name <- z@meta.data$sce_name[1]
  n_cells <- z@meta.data$n_cells[1]
  n_genes <- z@meta.data$n_genes[1]
  perc_deg <- z@meta.data$perc_deg[1]
  # summary stat table 
  sparsity_count <- mean(z@assays$originalexp@counts == 0)
  mean_count <- mean(z@assays$originalexp@counts)
  med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
  sd_count <- sd(z@assays$originalexp@counts)
  var_count <- sd_count^2 
  range_count <- range(z@assays$originalexp@counts)
  summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                           value = c(round(mean_count, 2), 
                                     round(med_count, 2), 
                                     round(sd_count, 2), 
                                     round(var_count, 2),
                                     paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                     paste0(round(sparsity_count, 4) * 100, "%")))
  # create counts histogram
  p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "dodgerblue") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        scale_x_continuous(labels = scales::label_comma()) + 
        labs(x = "Raw Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create log counts histogram
  p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "forestgreen") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        labs(x = "Normalized Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create UMAP by cluster 
  p2 <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                   UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                   cluster = z$label) %>% 
        ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
        geom_point() + 
        scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
        labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") + 
        theme_classic(base_size = 14) + 
        theme(axis.text = element_blank(), 
              axis.ticks = element_blank()) + 
        guides(color = guide_legend(override.aes = list(size = 2)))
  # create PCA of cell ordering 
  p3 <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                   PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                   cell_time = z$cell_time_normed) %>% 
        ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
        geom_point() + 
        scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
        labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
        theme_classic(base_size = 14) + 
        theme(axis.text = element_blank(), 
              axis.ticks = element_blank())
  # table of simulation parameters 
  param_df <- data.frame(metric = c("Number of Cells", "Number of Genes", "% Dynamic Genes"), 
                         value = c(as.character(n_cells), as.character(n_genes), perc_deg))
  plot_table <- rbind(summary_df, param_df) %>% 
                gridExtra::tableGrob(rows = NULL, 
                                     cols = c("Metric", "Value"), 
                                     theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                       colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 2, 
                                        b = nrow(.), 
                                        l = 1, 
                                        r = ncol(.)) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 1, 
                                        l = 1, 
                                        r = ncol(.))
  # align everything 
  p4a <- (p0 | p1) / (p2 | p3) + 
         plot_layout(guides = "collect")
  p4b <- (p4a | plot_table) + 
         plot_layout(ncol = 2, widths = c(3, 1)) + 
         plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
  # save & print plot
  ggsave(filename = paste0("QC_", obj_name, ".pdf"),
         plot = p4b, 
         device = "pdf", 
         path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
         width = 13,
         height = 8, 
         units = "in", 
         dpi = "retina")
  print(p4b)
  # cleanup 
  sink(tempfile())
  rm(p0, p1, p2, p3, p4a, p4b, plot_table); gc(full = TRUE)
  sink()
})
rm(obj_list)
```

## Multi-subject 

We repeat the process for the multi-subject simulated datasets. 

```{r, results='hide'}
# 100 cells
tar_load(lung_sim_DEG_10_CELLS_100_balanced)
tar_load(lung_sim_DEG_20_CELLS_100_balanced)
tar_load(lung_sim_DEG_10_CELLS_100_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_100_unbalanced)
# 500 cells
tar_load(lung_sim_DEG_10_CELLS_500_balanced)
tar_load(lung_sim_DEG_20_CELLS_500_balanced)
tar_load(lung_sim_DEG_10_CELLS_500_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_500_unbalanced)
# 1,000 cells 
tar_load(lung_sim_DEG_10_CELLS_1000_balanced)
tar_load(lung_sim_DEG_20_CELLS_1000_balanced)
tar_load(lung_sim_DEG_10_CELLS_1000_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_1000_unbalanced)
# 2,500 cells
tar_load(lung_sim_DEG_10_CELLS_2500_balanced)
tar_load(lung_sim_DEG_20_CELLS_2500_balanced)
tar_load(lung_sim_DEG_10_CELLS_2500_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_2500_unbalanced)
# 5,000 cells 
tar_load(lung_sim_DEG_10_CELLS_5000_balanced)
tar_load(lung_sim_DEG_20_CELLS_5000_balanced)
tar_load(lung_sim_DEG_10_CELLS_5000_unbalanced)
tar_load(lung_sim_DEG_20_CELLS_5000_unbalanced)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "lung_sim*balanced"), function(sim) {
  obj <- eval(as.symbol(sim))
  obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
  obj@meta.data <- mutate(obj@meta.data, 
                          perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "lung_sim_DEG_.."), "lung_sim_DEG_")), "%"), 
                          n_cells = as.character(ncol(obj)), 
                          n_genes = as.character(nrow(obj)), 
                          allocation = ifelse(grepl("_balanced", sim), "Balanced", "Unbalanced"), 
                          sce_name = sim)
  return(obj)
})
rm(list = ls(pattern = "lung_sim")); gc(full = TRUE)
```

We add a UMAP of the subject identities, & a PCA plot of the true pseudotime split by subject when generating the QC plots for multi-subject data. 

```{r, fig.width=13, fig.height=9, results='hold'}
purrr::walk(obj_list, function(z) {
  # gather metadata 
  obj_name <- z@meta.data$sce_name[1]
  n_cells <- z@meta.data$n_cells[1]
  n_genes <- z@meta.data$n_genes[1]
  perc_deg <- z@meta.data$perc_deg[1]
  allocation <- z@meta.data$allocation[1]
  n_subjects <- length(unique(z@meta.data$subject))
  # summary stat table 
  sparsity_count <- mean(z@assays$originalexp@counts == 0)
  mean_count <- mean(z@assays$originalexp@counts)
  med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
  sd_count <- sd(z@assays$originalexp@counts)
  var_count <- sd_count^2 
  range_count <- range(z@assays$originalexp@counts)
  summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                           value = c(round(mean_count, 2), 
                                     round(med_count, 2), 
                                     round(sd_count, 2), 
                                     round(var_count, 2),
                                     paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                     paste0(round(sparsity_count, 4) * 100, "%")))
  # create counts histogram
  p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "dodgerblue") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        scale_x_continuous(labels = scales::label_comma()) + 
        labs(x = "Raw Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create log counts histogram
  p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "forestgreen") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        labs(x = "Normalized Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create UMAP by cluster & subject
  legend_clust <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                                                     UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                                                     cluster = z$label) %>% 
                                          ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
                                          geom_point() + 
                                          scale_color_manual(values = paletteer_d("ggsci::category20_d3")[1:length(unique(z$label))]) + 
                                          labs(color = "Louvain\nCluster") + 
                                          theme_classic(base_size = 14) + theme(legend.text.align = 0.5)))
                                          
  legend_subj <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                                                    UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                                                    subject = z$subject) %>% 
                                         ggplot(aes(x = UMAP1, y = UMAP2, color = subject)) + 
                                         geom_point() + 
                                         scale_color_manual(values = paletteer_d("ggsci::category20_d3")[(length(unique(z$label)) + 1):20]) + 
                                         labs(color = "Subject") + 
                                         theme_classic(base_size = 14)))
  p2a <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                    UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                    cluster = z$label, 
                    subject = z$subject) %>% 
           tidyr::pivot_longer(cols = c(cluster, subject), names_to = "ident", values_to = "ident_value") %>% 
           mutate(ident = case_when(ident == "cluster" ~ "Louvain Cluster", 
                                    TRUE ~ "Subject")) %>% 
           ggplot(aes(x = UMAP1, y = UMAP2, color = ident_value, group = ident)) + 
           facet_wrap(~ident) + 
           geom_point() + 
           scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
           labs(x = "UMAP 1", y = "UMAP 2") + 
           theme_classic(base_size = 14) + 
           theme(legend.position = "none", 
                 axis.text = element_blank(), 
                 axis.ticks = element_blank())
  p2b <- (p2a + (wrap_elements(legend_clust) / wrap_elements(legend_subj)) + 
         plot_layout(ncol = 2, widths = c(4, 1)))
  # create PCA of cell ordering 
  p3a <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                    PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                    cell_time = z$cell_time_normed, 
                    subject = z$subject) %>% 
         ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
         facet_wrap(~subject) + 
         geom_point() + 
         scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
         labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
         theme_classic(base_size = 14) + 
         theme(axis.ticks = element_blank(), 
               axis.text = element_blank())
  p3b <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                    PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                    cell_time = z$cell_time_normed) %>% 
       ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
       geom_point() + 
       scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
       labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
       theme_classic(base_size = 14) + 
       theme(axis.ticks = element_blank(), 
             axis.text = element_blank())
  p3c <- (p3a | p3b) + 
         plot_layout(guides = "collect", widths = c(3, 2), ncol = 2)
  # table of simulation parameters 
  param_df <- data.frame(metric = c("N Cells", "N Genes", "% Dynamic", "Allocation", "N Subjects"), 
                         value = c(as.character(n_cells), 
                                   as.character(n_genes),
                                   perc_deg, 
                                   allocation, 
                                   n_subjects))
  plot_table <- rbind(summary_df, param_df) %>% 
                gridExtra::tableGrob(rows = NULL, 
                                     cols = c("Metric", "Value"), 
                                     theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                       colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 2, 
                                        b = nrow(.), 
                                        l = 1, 
                                        r = ncol(.)) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 1, 
                                        l = 1, 
                                        r = ncol(.))
  # align everything 
  p4a <- ((p0 | p1 | plot_table) + plot_layout(widths = c(1, 1, 0.5))) / (p2b | p3c) + 
         plot_layout(nrow = 2) + 
         plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
  # save & print plot
  ggsave(filename = paste0("QC_", obj_name, ".pdf"),
         plot = p4a, 
         device = "pdf", 
         path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
         width = 13,
         height = 9, 
         units = "in", 
         dpi = "retina")
  print(p4a)
  # cleanup 
  sink(tempfile())
  rm(p0, p1, p2, p3, p4a, plot_table); gc(full = TRUE)
  sink()
})
rm(obj_list)
```

# Pancreas Reference 

Next let's examine the pancreas reference dataset. Since it's smaller than the lung reference, we don't need to resort to trickery to generate our histograms. 

```{r}
panc_data <- scRNAseq::BaronPancreasData(which = "human")
panc_data_clean <- panc_data[rowSums(counts(panc_data) > 0) >= 3, ]  # genes in at least 3 cells
panc_data_clean <- logNormCounts(panc_data_clean)
var_decomp <- modelGeneVar(panc_data_clean)
top2k_hvgs <- getTopHVGs(var_decomp, n = 2000)
panc_data_clean <- runPCA(panc_data_clean, subset_row = top2k_hvgs)
reducedDim(panc_data_clean, "PCAsub") <- reducedDim(panc_data_clean, "PCA")[, 1:30, drop = FALSE]
panc_data_clean <- runUMAP(panc_data_clean, 
                           dimred = "PCAsub", 
                           n_dimred = 1:30)
g <- buildSNNGraph(panc_data_clean,
                   use.dimred = "PCAsub",
                   k = 30)
clusters <- igraph::cluster_louvain(graph = g)$membership
colLabels(panc_data_clean) <- factor(clusters)
panc_data_clean <- as.Seurat(panc_data_clean, 
                             counts = "counts", 
                             data = "logcounts")
```

We re-create the summary statistics table for the pancreas reference. 

```{r, results='hide'}
n_cells <- ncol(panc_data_clean)
n_genes <- nrow(panc_data_clean)
mean_count <- mean(panc_data_clean@assays$originalexp@counts)
med_count <- 0
sd_count <- sd(panc_data_clean@assays$originalexp@counts)
var_count <- sd_count^2  # faster 
range_count <- range(panc_data_clean@assays$originalexp@counts)
sparsity_count <- mean(panc_data_clean@assays$originalexp@counts == 0)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                         value = c(round(mean_count, 2), 
                                   med_count, 
                                   round(sd_count, 2), 
                                   round(var_count, 2),
                                   paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                   paste0(round(sparsity_count, 4) * 100, "%")))
plot_table <- gridExtra::tableGrob(summary_df, rows = NULL, 
                                   cols = c("Metric", "Value"), 
                                   theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                     colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
              gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                      t = 2, 
                                      b = nrow(.), 
                                      l = 1, 
                                      r = ncol(.)) %>% 
              gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                      t = 1, 
                                      l = 1, 
                                      r = ncol(.))
gc(full = TRUE)
```

We create the two histograms in the usual way.

```{r}
p0 <- data.frame(x = as.numeric(panc_data_clean@assays$originalexp@counts)) %>% 
      ggplot(aes(x = x)) + 
      geom_histogram(fill = "dodgerblue") + 
      scale_y_continuous(labels = scales::label_scientific()) + 
      scale_x_continuous(labels = scales::label_comma()) + 
      labs(x = "Raw Expression", y = "Frequency") + 
      theme_classic(base_size = 14)
p1 <- data.frame(x = as.numeric(panc_data_clean@assays$originalexp@data)) %>% 
      ggplot(aes(x = x)) + 
      geom_histogram(fill = "forestgreen") + 
      scale_y_continuous(labels = scales::label_scientific()) + 
      labs(x = "Normalized Expression", y = "Frequency") + 
      theme_classic(base_size = 14)
```

The UMAP & PCA plots are generated as before. 

```{r}
p2 <- data.frame(UMAP1 = panc_data_clean@reductions$UMAP@cell.embeddings[, 1], 
                 UMAP2 = panc_data_clean@reductions$UMAP@cell.embeddings[, 2], 
                 cluster = panc_data_clean$label) %>% 
      ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
      geom_point() + 
      scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
      labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") + 
      theme_classic(base_size = 14) + 
      theme(axis.text = element_blank(), 
            axis.ticks = element_blank()) +
      guides(color = guide_legend(override.aes = list(size = 2)))
p3 <- data.frame(PC1 = panc_data_clean@reductions$PCA@cell.embeddings[, 1], 
                 PC2 = panc_data_clean@reductions$PCA@cell.embeddings[, 2], 
                 cluster = panc_data_clean$label) %>% 
      ggplot(aes(x = PC1, y = PC2, color = cluster)) + 
      geom_point() + 
      scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
      labs(x = "PC 1", y = "PC 2", color = "Louvain Cluster") + 
      theme_classic(base_size = 14) + 
      theme(legend.position = "none", 
            axis.text = element_blank(), 
            axis.ticks = element_blank())
```

Lastly, we align everything & plot it. 

```{r, fig.width=13, fig.height=8}
p4a <- (p0 | p1) / (p2 | p3) + 
       plot_layout(guides = "collect")
p4b <- (p4a | plot_table) + 
       plot_layout(ncol = 2, widths = c(3, 1)) + 
       plot_annotation(title = paste0("Metrics for Pancreas Reference Dataset"))
ggsave(filename = "QC_panc_reference.pdf",
       plot = p4b, 
       device = "pdf", 
       path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
       width = 13,
       height = 8, 
       units = "in", 
       dpi = "retina")
p4b
```

## Single-subject

Here we bring the single subject simulations from the pancreas reference into memory. 

```{r, results='hide'}
# 100 cells 
tar_load(panc_sim_DEG_01_CELLS_100)
tar_load(panc_sim_DEG_05_CELLS_100)
tar_load(panc_sim_DEG_10_CELLS_100)
tar_load(panc_sim_DEG_20_CELLS_100)
# 500 cells 
tar_load(panc_sim_DEG_01_CELLS_500)
tar_load(panc_sim_DEG_05_CELLS_500)
tar_load(panc_sim_DEG_10_CELLS_500)
tar_load(panc_sim_DEG_20_CELLS_500)
# 1,000 cells 
tar_load(panc_sim_DEG_01_CELLS_1000)
tar_load(panc_sim_DEG_05_CELLS_1000)
tar_load(panc_sim_DEG_10_CELLS_1000)
tar_load(panc_sim_DEG_20_CELLS_1000)
# 2,500 cells
tar_load(panc_sim_DEG_01_CELLS_2500)
tar_load(panc_sim_DEG_05_CELLS_2500)
tar_load(panc_sim_DEG_10_CELLS_2500)
tar_load(panc_sim_DEG_20_CELLS_2500)
# 5,000 cells 
tar_load(panc_sim_DEG_01_CELLS_5000)
tar_load(panc_sim_DEG_05_CELLS_5000)
tar_load(panc_sim_DEG_10_CELLS_5000)
tar_load(panc_sim_DEG_20_CELLS_5000)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "panc_sim")[!grepl("balanced", ls(pattern = "panc_sim"))], function(sim) {
  obj <- eval(as.symbol(sim))
  obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
  obj@meta.data <- mutate(obj@meta.data, 
                          perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "panc_sim_DEG_.."), "panc_sim_DEG_")), "%"), 
                          n_cells = as.character(ncol(obj)), 
                          n_genes = as.character(nrow(obj)), 
                          sce_name = sim)
  return(obj)
})
rm(list = ls(pattern = "panc_sim")); gc(full = TRUE)
```

Let's generate the plots for each dataset. 

```{r, fig.width=13, fig.height=8, results='hold'}
purrr::walk(obj_list, function(z) {
  # gather metadata 
  obj_name <- z@meta.data$sce_name[1]
  n_cells <- z@meta.data$n_cells[1]
  n_genes <- z@meta.data$n_genes[1]
  perc_deg <- z@meta.data$perc_deg[1]
  # summary stat table 
  sparsity_count <- mean(z@assays$originalexp@counts == 0)
  mean_count <- mean(z@assays$originalexp@counts)
  med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
  sd_count <- sd(z@assays$originalexp@counts)
  var_count <- sd_count^2 
  range_count <- range(z@assays$originalexp@counts)
  summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                           value = c(round(mean_count, 2), 
                                     round(med_count, 2), 
                                     round(sd_count, 2), 
                                     round(var_count, 2),
                                     paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                     paste0(round(sparsity_count, 4) * 100, "%")))
  # create counts histogram
  p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "dodgerblue") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        scale_x_continuous(labels = scales::label_comma()) + 
        labs(x = "Raw Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create log counts histogram
  p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "forestgreen") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        labs(x = "Normalized Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create UMAP by cluster 
  p2 <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                   UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                   cluster = z$label) %>% 
        ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
        geom_point() + 
        scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
        labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") + 
        theme_classic(base_size = 14) + 
        theme(axis.text = element_blank(), 
              axis.ticks = element_blank()) + 
        guides(color = guide_legend(override.aes = list(size = 2)))
  # create PCA of cell ordering 
  p3 <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                   PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                   cell_time = z$cell_time_normed) %>% 
        ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
        geom_point() + 
        scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
        labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
        theme_classic(base_size = 14) + 
        theme(axis.text = element_blank(), 
              axis.ticks = element_blank())
  # table of simulation parameters 
  param_df <- data.frame(metric = c("Number of Cells", "Number of Genes", "% Dynamic Genes"), 
                         value = c(as.character(n_cells), as.character(n_genes), perc_deg))
  plot_table <- rbind(summary_df, param_df) %>% 
                gridExtra::tableGrob(rows = NULL, 
                                     cols = c("Metric", "Value"), 
                                     theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                       colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 2, 
                                        b = nrow(.), 
                                        l = 1, 
                                        r = ncol(.)) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 1, 
                                        l = 1, 
                                        r = ncol(.))
  # align everything 
  p4a <- (p0 | p1) / (p2 | p3) + 
         plot_layout(guides = "collect")
  p4b <- (p4a | plot_table) + 
         plot_layout(ncol = 2, widths = c(3, 1)) + 
         plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
  # save & print plot
  ggsave(filename = paste0("QC_", obj_name, ".pdf"),
         plot = p4b, 
         device = "pdf", 
         path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
         width = 13,
         height = 8, 
         units = "in", 
         dpi = "retina")
  print(p4b)
  # cleanup 
  sink(tempfile())
  rm(p0, p1, p2, p3, p4a, p4b, plot_table); gc(full = TRUE)
  sink()
})
rm(obj_list)
```

## Multi-subject 

We load in the multi-subject simulated datasets from the pancreas reference. 

```{r, results='hide'}
# 100 cells
tar_load(panc_sim_DEG_10_CELLS_100_balanced)
tar_load(panc_sim_DEG_20_CELLS_100_balanced)
tar_load(panc_sim_DEG_10_CELLS_100_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_100_unbalanced)
# 500 cells
tar_load(panc_sim_DEG_10_CELLS_500_balanced)
tar_load(panc_sim_DEG_20_CELLS_500_balanced)
tar_load(panc_sim_DEG_10_CELLS_500_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_500_unbalanced)
# 1,000 cells 
tar_load(panc_sim_DEG_10_CELLS_1000_balanced)
tar_load(panc_sim_DEG_20_CELLS_1000_balanced)
tar_load(panc_sim_DEG_10_CELLS_1000_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_1000_unbalanced)
# 2,500 cells
tar_load(panc_sim_DEG_10_CELLS_2500_balanced)
tar_load(panc_sim_DEG_20_CELLS_2500_balanced)
tar_load(panc_sim_DEG_10_CELLS_2500_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_2500_unbalanced)
# 5,000 cells 
tar_load(panc_sim_DEG_10_CELLS_5000_balanced)
tar_load(panc_sim_DEG_20_CELLS_5000_balanced)
tar_load(panc_sim_DEG_10_CELLS_5000_unbalanced)
tar_load(panc_sim_DEG_20_CELLS_5000_unbalanced)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "panc_sim_*balanced"), function(sim) {
  obj <- eval(as.symbol(sim))
  obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
  obj@meta.data <- mutate(obj@meta.data, 
                          perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "panc_sim_DEG_.."), "panc_sim_DEG_")), "%"), 
                          n_cells = as.character(ncol(obj)), 
                          n_genes = as.character(nrow(obj)), 
                          allocation = ifelse(grepl("_balanced", sim), "Balanced", "Unbalanced"), 
                          sce_name = sim)
  return(obj)
})
rm(list = ls(pattern = "panc_sim")); gc(full = TRUE)
```

We regenerate the slightly more complex multi-subject figures as we did for the lung dataset. 

```{r, fig.width=13, fig.height=9, results='hold'}
purrr::walk(obj_list, function(z) {
  # gather metadata 
  obj_name <- z@meta.data$sce_name[1]
  n_cells <- z@meta.data$n_cells[1]
  n_genes <- z@meta.data$n_genes[1]
  perc_deg <- z@meta.data$perc_deg[1]
  allocation <- z@meta.data$allocation[1]
  n_subjects <- length(unique(z@meta.data$subject))
  # summary stat table 
  sparsity_count <- mean(z@assays$originalexp@counts == 0)
  mean_count <- mean(z@assays$originalexp@counts)
  med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
  sd_count <- sd(z@assays$originalexp@counts)
  var_count <- sd_count^2 
  range_count <- range(z@assays$originalexp@counts)
  summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                           value = c(round(mean_count, 2), 
                                     round(med_count, 2), 
                                     round(sd_count, 2), 
                                     round(var_count, 2),
                                     paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                     paste0(round(sparsity_count, 4) * 100, "%")))
  # create counts histogram
  p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "dodgerblue") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        scale_x_continuous(labels = scales::label_comma()) + 
        labs(x = "Raw Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create log counts histogram
  p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "forestgreen") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        labs(x = "Normalized Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create UMAP by cluster & subject
  legend_clust <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                                                     UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                                                     cluster = z$label) %>% 
                                          ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
                                          geom_point() + 
                                          scale_color_manual(values = paletteer_d("ggsci::category20_d3")[1:length(unique(z$label))]) + 
                                          labs(color = "Louvain\nCluster") + 
                                          theme_classic(base_size = 14) + theme(legend.text.align = 0.5)))
                                          
  legend_subj <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                                                    UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                                                    subject = z$subject) %>% 
                                         ggplot(aes(x = UMAP1, y = UMAP2, color = subject)) + 
                                         geom_point() + 
                                         scale_color_manual(values = paletteer_d("ggsci::category20_d3")[(length(unique(z$label)) + 1):20]) + 
                                         labs(color = "Subject") + 
                                         theme_classic(base_size = 14)))
  p2a <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                    UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                    cluster = z$label, 
                    subject = z$subject) %>% 
           tidyr::pivot_longer(cols = c(cluster, subject), names_to = "ident", values_to = "ident_value") %>% 
           mutate(ident = case_when(ident == "cluster" ~ "Louvain Cluster", 
                                    TRUE ~ "Subject")) %>% 
           ggplot(aes(x = UMAP1, y = UMAP2, color = ident_value, group = ident)) + 
           facet_wrap(~ident) + 
           geom_point() + 
           scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
           labs(x = "UMAP 1", y = "UMAP 2") + 
           theme_classic(base_size = 14) + 
           theme(legend.position = "none", 
                 axis.text = element_blank(), 
                 axis.ticks = element_blank())
  p2b <- (p2a + (wrap_elements(legend_clust) / wrap_elements(legend_subj)) + 
         plot_layout(ncol = 2, widths = c(4, 1)))
  # create PCA of cell ordering 
  p3a <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                    PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                    cell_time = z$cell_time_normed, 
                    subject = z$subject) %>% 
         ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
         facet_wrap(~subject) + 
         geom_point() + 
         scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
         labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
         theme_classic(base_size = 14) + 
         theme(axis.ticks = element_blank(), 
               axis.text = element_blank())
  p3b <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                    PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                    cell_time = z$cell_time_normed) %>% 
       ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
       geom_point() + 
       scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
       labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
       theme_classic(base_size = 14) + 
       theme(axis.ticks = element_blank(), 
             axis.text = element_blank())
  p3c <- (p3a | p3b) + 
         plot_layout(guides = "collect", widths = c(3, 2), ncol = 2)
  # table of simulation parameters 
  param_df <- data.frame(metric = c("N Cells", "N Genes", "% Dynamic", "Allocation", "N Subjects"), 
                         value = c(as.character(n_cells), 
                                   as.character(n_genes),
                                   perc_deg, 
                                   allocation, 
                                   n_subjects))
  plot_table <- rbind(summary_df, param_df) %>% 
                gridExtra::tableGrob(rows = NULL, 
                                     cols = c("Metric", "Value"), 
                                     theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                       colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 2, 
                                        b = nrow(.), 
                                        l = 1, 
                                        r = ncol(.)) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 1, 
                                        l = 1, 
                                        r = ncol(.))
  # align everything 
  p4a <- ((p0 | p1 | plot_table) + plot_layout(widths = c(1, 1, 0.5))) / (p2b | p3c) + 
         plot_layout(nrow = 2) + 
         plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
  # save & print plot
  ggsave(filename = paste0("QC_", obj_name, ".pdf"),
         plot = p4b, 
         device = "pdf", 
         path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
         width = 13,
         height = 9, 
         units = "in", 
         dpi = "retina")
  print(p4a)
  # cleanup 
  sink(tempfile())
  rm(p0, p1, p2a, p2b, p3a, p3b, p3c, p4a, plot_table); gc(full = TRUE)
  sink()
})
rm(obj_list)
```

# Pancreatic Endocrinogenesis Reference 

First let's take a look at the reference dataset itself. Loading this one is a bit more complicated - it comes as part of the `scVelo` Python library, so we use the `{reticulate}` R package to access & load it before converting it to a `SingleCellExperiment` object using the `{zellkonverter}` package. Though the data comes pre-processed, we reprocess it using the same steps as we did on the other two datasets to ensure a good comparison. A necessary wrinkle here is the activation of a `conda` environment that I created previously & installed `scVelo` and its dependencies into. 

```{r}
reticulate::use_condaenv(condaenv = "HPG_venv",
                         conda = "/apps/conda/22.11.1/condabin/conda", 
                         required = TRUE)
scvelo <- reticulate::import("scvelo")
adata <- scvelo$datasets$pancreas()
endo <- zellkonverter::AnnData2SCE(adata = adata)
endo@assays@data$X <- NULL
endo@assays@data$counts <- endo@assays@data$spliced
endo_data_clean <- endo[rowSums(SingleCellExperiment::counts(endo) > 0) >= 3, ]
endo_data_clean <- logNormCounts(endo_data_clean)
var_decomp <- modelGeneVar(endo_data_clean)
top2k_hvgs <- getTopHVGs(var_decomp, n = 2000)
endo_data_clean <- runPCA(endo_data_clean, subset_row = top2k_hvgs)
reducedDim(endo_data_clean, "PCAsub") <- reducedDim(endo_data_clean, "PCA")[, 1:30, drop = FALSE]
endo_data_clean <- runUMAP(endo_data_clean, 
                           dimred = "PCAsub", 
                           n_dimred = 1:30)
g <- buildSNNGraph(endo_data_clean,
                   use.dimred = "PCAsub",
                   k = 30)
clusters <- igraph::cluster_louvain(graph = g)$membership
colLabels(endo_data_clean) <- factor(clusters)
endo_data_clean <- as.Seurat(endo_data_clean, 
                             counts = "counts", 
                             data = "logcounts")
```

We re-create the summary statistics table for the pancreas reference. 

```{r, results='hide'}
n_cells <- ncol(endo_data_clean)
n_genes <- nrow(endo_data_clean)
mean_count <- mean(endo_data_clean@assays$originalexp@counts)
med_count <- 0
sd_count <- sd(endo_data_clean@assays$originalexp@counts)
var_count <- sd_count^2  # faster 
range_count <- range(endo_data_clean@assays$originalexp@counts)
sparsity_count <- mean(endo_data_clean@assays$originalexp@counts == 0)
summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                         value = c(round(mean_count, 2), 
                                   med_count, 
                                   round(sd_count, 2), 
                                   round(var_count, 2),
                                   paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                   paste0(round(sparsity_count, 4) * 100, "%")))
plot_table <- gridExtra::tableGrob(summary_df, rows = NULL, 
                                   cols = c("Metric", "Value"), 
                                   theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                     colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
              gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                      t = 2, 
                                      b = nrow(.), 
                                      l = 1, 
                                      r = ncol(.)) %>% 
              gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                      t = 1, 
                                      l = 1, 
                                      r = ncol(.))
gc(full = TRUE)
```

Like the pancreas reference, this dataset is small enough that we don't need to do any trickery to produce histograms. 

```{r}
p0 <- data.frame(x = as.numeric(endo_data_clean@assays$originalexp@counts)) %>% 
      ggplot(aes(x = x)) + 
      geom_histogram(fill = "dodgerblue") + 
      scale_y_continuous(labels = scales::label_scientific()) + 
      scale_x_continuous(labels = scales::label_comma()) + 
      labs(x = "Raw Expression", y = "Frequency") + 
      theme_classic(base_size = 14)
p1 <- data.frame(x = as.numeric(endo_data_clean@assays$originalexp@data)) %>% 
      ggplot(aes(x = x)) + 
      geom_histogram(fill = "forestgreen") + 
      scale_y_continuous(labels = scales::label_scientific()) + 
      labs(x = "Normalized Expression", y = "Frequency") + 
      theme_classic(base_size = 14)
```

The UMAP & PCA plots are generated as before. 

```{r}
p2 <- data.frame(UMAP1 = endo_data_clean@reductions$UMAP@cell.embeddings[, 1], 
                 UMAP2 = endo_data_clean@reductions$UMAP@cell.embeddings[, 2], 
                 cluster = endo_data_clean$label) %>% 
      ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
      geom_point() + 
      scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
      labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") + 
      theme_classic(base_size = 14) + 
      theme(axis.text = element_blank(), 
            axis.ticks = element_blank()) +
      guides(color = guide_legend(override.aes = list(size = 2)))
p3 <- data.frame(PC1 = endo_data_clean@reductions$PCA@cell.embeddings[, 1], 
                 PC2 = endo_data_clean@reductions$PCA@cell.embeddings[, 2], 
                 cluster = endo_data_clean$label) %>% 
      ggplot(aes(x = PC1, y = PC2, color = cluster)) + 
      geom_point() + 
      scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
      labs(x = "PC 1", y = "PC 2", color = "Louvain Cluster") + 
      theme_classic(base_size = 14) + 
      theme(legend.position = "none", 
            axis.text = element_blank(), 
            axis.ticks = element_blank())
p3
```

We save the final plot as a PDF & display it. 

```{r, fig.width=13, fig.height=8}
p4a <- (p0 | p1) / (p2 | p3) + 
       plot_layout(guides = "collect")
p4b <- (p4a | plot_table) + 
       plot_layout(ncol = 2, widths = c(3, 1)) + 
       plot_annotation(title = paste0("Metrics for Endocrinogenesis Reference Dataset"))
ggsave(filename = "QC_endo_reference.pdf",
       plot = p4b, 
       device = "pdf", 
       path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
       width = 13,
       height = 8, 
       units = "in", 
       dpi = "retina")
p4b
```

## Single-subject 

We load the single-subject endocrinogenesis datasets. 

```{r, results='hide'}
# 100 cells 
tar_load(endo_sim_DEG_01_CELLS_100)
tar_load(endo_sim_DEG_05_CELLS_100)
tar_load(endo_sim_DEG_10_CELLS_100)
tar_load(endo_sim_DEG_20_CELLS_100)
# 500 cells 
tar_load(endo_sim_DEG_01_CELLS_500)
tar_load(endo_sim_DEG_05_CELLS_500)
tar_load(endo_sim_DEG_10_CELLS_500)
tar_load(endo_sim_DEG_20_CELLS_500)
# 1,000 cells 
tar_load(endo_sim_DEG_01_CELLS_1000)
tar_load(endo_sim_DEG_05_CELLS_1000)
tar_load(endo_sim_DEG_10_CELLS_1000)
tar_load(endo_sim_DEG_20_CELLS_1000)
# 2,500 cells
tar_load(endo_sim_DEG_01_CELLS_2500)
tar_load(endo_sim_DEG_05_CELLS_2500)
tar_load(endo_sim_DEG_10_CELLS_2500)
tar_load(endo_sim_DEG_20_CELLS_2500)
# 5,000 cells 
tar_load(endo_sim_DEG_01_CELLS_5000)
tar_load(endo_sim_DEG_05_CELLS_5000)
tar_load(endo_sim_DEG_10_CELLS_5000)
tar_load(endo_sim_DEG_20_CELLS_5000)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "endo_sim")[!grepl("balanced", ls(pattern = "endo_sim"))], function(sim) {
  obj <- eval(as.symbol(sim))
  obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
  obj@meta.data <- mutate(obj@meta.data, 
                          perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "endo_sim_DEG_.."), "endo_sim_DEG_")), "%"), 
                          n_cells = as.character(ncol(obj)), 
                          n_genes = as.character(nrow(obj)), 
                          sce_name = sim)
  return(obj)
})
rm(list = ls(pattern = "endo_sim")); gc(full = TRUE)
```

We generate the plots for each dataset. 

```{r, fig.width=13, fig.height=8, results='hold'}
purrr::walk(obj_list, function(z) {
  # gather metadata 
  obj_name <- z@meta.data$sce_name[1]
  n_cells <- z@meta.data$n_cells[1]
  n_genes <- z@meta.data$n_genes[1]
  perc_deg <- z@meta.data$perc_deg[1]
  # summary stat table 
  sparsity_count <- mean(z@assays$originalexp@counts == 0)
  mean_count <- mean(z@assays$originalexp@counts)
  med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
  sd_count <- sd(z@assays$originalexp@counts)
  var_count <- sd_count^2 
  range_count <- range(z@assays$originalexp@counts)
  summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                           value = c(round(mean_count, 2), 
                                     round(med_count, 2), 
                                     round(sd_count, 2), 
                                     round(var_count, 2),
                                     paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                     paste0(round(sparsity_count, 4) * 100, "%")))
  # create counts histogram
  p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "dodgerblue") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        scale_x_continuous(labels = scales::label_comma()) + 
        labs(x = "Raw Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create log counts histogram
  p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "forestgreen") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        labs(x = "Normalized Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create UMAP by cluster 
  p2 <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                   UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                   cluster = z$label) %>% 
        ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
        geom_point() + 
        scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
        labs(x = "UMAP 1", y = "UMAP 2", color = "Louvain Cluster") + 
        theme_classic(base_size = 14) + 
        theme(axis.text = element_blank(), 
              axis.ticks = element_blank()) + 
        guides(color = guide_legend(override.aes = list(size = 2)))
  # create PCA of cell ordering 
  p3 <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                   PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                   cell_time = z$cell_time_normed) %>% 
        ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
        geom_point() + 
        scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
        labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
        theme_classic(base_size = 14) + 
        theme(axis.text = element_blank(), 
              axis.ticks = element_blank())
  # table of simulation parameters 
  param_df <- data.frame(metric = c("Number of Cells", "Number of Genes", "% Dynamic Genes"), 
                         value = c(as.character(n_cells), as.character(n_genes), perc_deg))
  plot_table <- rbind(summary_df, param_df) %>% 
                gridExtra::tableGrob(rows = NULL, 
                                     cols = c("Metric", "Value"), 
                                     theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                       colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 2, 
                                        b = nrow(.), 
                                        l = 1, 
                                        r = ncol(.)) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 1, 
                                        l = 1, 
                                        r = ncol(.))
  # align everything 
  p4a <- (p0 | p1) / (p2 | p3) + 
         plot_layout(guides = "collect")
  p4b <- (p4a | plot_table) + 
         plot_layout(ncol = 2, widths = c(3, 1)) + 
         plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
  # save & print plot
  ggsave(filename = paste0("QC_", obj_name, ".pdf"),
         plot = p4b, 
         device = "pdf", 
         path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
         width = 13,
         height = 8, 
         units = "in", 
         dpi = "retina")
  print(p4b)
  # cleanup 
  sink(tempfile())
  rm(p0, p1, p2, p3, p4a, p4b, plot_table); gc(full = TRUE)
  sink()
})
rm(obj_list)
```

## Multi-subject 

Lastly, we bring the multi-subject simulated datasets into memory. 

```{r, results='hide'}
# 100 cells
tar_load(endo_sim_DEG_10_CELLS_100_balanced)
tar_load(endo_sim_DEG_20_CELLS_100_balanced)
tar_load(endo_sim_DEG_10_CELLS_100_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_100_unbalanced)
# 500 cells
tar_load(endo_sim_DEG_10_CELLS_500_balanced)
tar_load(endo_sim_DEG_20_CELLS_500_balanced)
tar_load(endo_sim_DEG_10_CELLS_500_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_500_unbalanced)
# 1,000 cells 
tar_load(endo_sim_DEG_10_CELLS_1000_balanced)
tar_load(endo_sim_DEG_20_CELLS_1000_balanced)
tar_load(endo_sim_DEG_10_CELLS_1000_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_1000_unbalanced)
# 2,500 cells
tar_load(endo_sim_DEG_10_CELLS_2500_balanced)
tar_load(endo_sim_DEG_20_CELLS_2500_balanced)
tar_load(endo_sim_DEG_10_CELLS_2500_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_2500_unbalanced)
# 5,000 cells 
tar_load(endo_sim_DEG_10_CELLS_5000_balanced)
tar_load(endo_sim_DEG_20_CELLS_5000_balanced)
tar_load(endo_sim_DEG_10_CELLS_5000_unbalanced)
tar_load(endo_sim_DEG_20_CELLS_5000_unbalanced)
# coerce to list & process
obj_list <- purrr::map(ls(pattern = "endo_sim*balanced"), function(sim) {
  obj <- eval(as.symbol(sim))
  obj <- as.Seurat(obj, counts = "counts", data = "logcounts")
  obj@meta.data <- mutate(obj@meta.data, 
                          perc_deg = paste0(as.numeric(stringr::str_remove(stringr::str_extract(sim, "endo_sim_DEG_.."), "endo_sim_DEG_")), "%"), 
                          n_cells = as.character(ncol(obj)), 
                          n_genes = as.character(nrow(obj)), 
                          allocation = ifelse(grepl("_balanced", sim), "Balanced", "Unbalanced"), 
                          sce_name = sim)
  return(obj)
})
rm(list = ls(pattern = "endo_sim")); gc(full = TRUE)
```

We generate the final set of plots. 

```{r, fig.width=13, fig.height=9, results='hold'}
purrr::walk(obj_list, function(z) {
  # gather metadata 
  obj_name <- z@meta.data$sce_name[1]
  n_cells <- z@meta.data$n_cells[1]
  n_genes <- z@meta.data$n_genes[1]
  perc_deg <- z@meta.data$perc_deg[1]
  allocation <- z@meta.data$allocation[1]
  n_subjects <- length(unique(z@meta.data$subject))
  # summary stat table 
  sparsity_count <- mean(z@assays$originalexp@counts == 0)
  mean_count <- mean(z@assays$originalexp@counts)
  med_count <- ifelse(sparsity_count > 0.5, 0, median(z@assays$originalexp@counts))
  sd_count <- sd(z@assays$originalexp@counts)
  var_count <- sd_count^2 
  range_count <- range(z@assays$originalexp@counts)
  summary_df <- data.frame(metric = c("Mean", "Median", "S.D.", "Variance", "Range", "Sparsity"), 
                           value = c(round(mean_count, 2), 
                                     round(med_count, 2), 
                                     round(sd_count, 2), 
                                     round(var_count, 2),
                                     paste0("(", range_count[1], ", ", range_count[2], ")"), 
                                     paste0(round(sparsity_count, 4) * 100, "%")))
  # create counts histogram
  p0 <- data.frame(x = as.numeric(z@assays$originalexp@counts)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "dodgerblue") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        scale_x_continuous(labels = scales::label_comma()) + 
        labs(x = "Raw Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create log counts histogram
  p1 <- data.frame(x = as.numeric(z@assays$originalexp@data)) %>% 
        ggplot(aes(x = x)) + 
        geom_histogram(fill = "forestgreen") + 
        scale_y_continuous(labels = scales::label_scientific()) + 
        labs(x = "Normalized Expression", y = "Frequency") + 
        theme_classic(base_size = 14)
  # create UMAP by cluster & subject
  legend_clust <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                                                     UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                                                     cluster = z$label) %>% 
                                          ggplot(aes(x = UMAP1, y = UMAP2, color = cluster)) + 
                                          geom_point() + 
                                          scale_color_manual(values = paletteer_d("ggsci::category20_d3")[1:length(unique(z$label))]) + 
                                          labs(color = "Louvain\nCluster") + 
                                          theme_classic(base_size = 14) + theme(legend.text.align = 0.5)))
                                          
  legend_subj <- ggpubr::get_legend(p = (data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                                                    UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                                                    subject = z$subject) %>% 
                                         ggplot(aes(x = UMAP1, y = UMAP2, color = subject)) + 
                                         geom_point() + 
                                         scale_color_manual(values = paletteer_d("ggsci::category20_d3")[(length(unique(z$label)) + 1):20]) + 
                                         labs(color = "Subject") + 
                                         theme_classic(base_size = 14)))
  p2a <- data.frame(UMAP1 = z@reductions$UMAP@cell.embeddings[, 1], 
                    UMAP2 = z@reductions$UMAP@cell.embeddings[, 2], 
                    cluster = z$label, 
                    subject = z$subject) %>% 
           tidyr::pivot_longer(cols = c(cluster, subject), names_to = "ident", values_to = "ident_value") %>% 
           mutate(ident = case_when(ident == "cluster" ~ "Louvain Cluster", 
                                    TRUE ~ "Subject")) %>% 
           ggplot(aes(x = UMAP1, y = UMAP2, color = ident_value, group = ident)) + 
           facet_wrap(~ident) + 
           geom_point() + 
           scale_color_manual(values = paletteer_d("ggsci::category20_d3")) + 
           labs(x = "UMAP 1", y = "UMAP 2") + 
           theme_classic(base_size = 14) + 
           theme(legend.position = "none", 
                 axis.text = element_blank(), 
                 axis.ticks = element_blank())
  p2b <- (p2a + (wrap_elements(legend_clust) / wrap_elements(legend_subj)) + 
         plot_layout(ncol = 2, widths = c(4, 1)))
  # create PCA of cell ordering 
  p3a <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                    PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                    cell_time = z$cell_time_normed, 
                    subject = z$subject) %>% 
         ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
         facet_wrap(~subject) + 
         geom_point() + 
         scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
         labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
         theme_classic(base_size = 14) + 
         theme(axis.ticks = element_blank(), 
               axis.text = element_blank())
  p3b <- data.frame(PC1 = z@reductions$PCA@cell.embeddings[, 1], 
                    PC2 = z@reductions$PCA@cell.embeddings[, 2], 
                    cell_time = z$cell_time_normed) %>% 
       ggplot(aes(x = PC1, y = PC2, color = cell_time)) + 
       geom_point() + 
       scale_color_gradientn(colors = paletteer_d("wesanderson::Zissou1")) + 
       labs(x = "PC 1", y = "PC 2", color = "True Ordering") + 
       theme_classic(base_size = 14) + 
       theme(axis.ticks = element_blank(), 
             axis.text = element_blank())
  p3c <- (p3a | p3b) + 
         plot_layout(guides = "collect", widths = c(3, 2), ncol = 2)
  # table of simulation parameters 
  param_df <- data.frame(metric = c("N Cells", "N Genes", "% Dynamic", "Allocation", "N Subjects"), 
                         value = c(as.character(n_cells), 
                                   as.character(n_genes),
                                   perc_deg, 
                                   allocation, 
                                   n_subjects))
  plot_table <- rbind(summary_df, param_df) %>% 
                gridExtra::tableGrob(rows = NULL, 
                                     cols = c("Metric", "Value"), 
                                     theme = gridExtra::ttheme_minimal(core = list(fg_params = list(hjust = 0, x = 0.05)), 
                                                                       colhead = list(fg_params = list(hjust = 0, x = 0.05)))) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 2, 
                                        b = nrow(.), 
                                        l = 1, 
                                        r = ncol(.)) %>% 
                gtable::gtable_add_grob(grobs = grid::rectGrob(gp = grid::gpar(fill = NA, lwd = 2)), 
                                        t = 1, 
                                        l = 1, 
                                        r = ncol(.))
  # align everything 
  p4a <- ((p0 | p1 | plot_table) + plot_layout(widths = c(1, 1, 0.5))) / (p2b | p3c) + 
         plot_layout(nrow = 2) + 
         plot_annotation(title = paste0("Metrics for dataset: ", obj_name))
  # save & print plot
  ggsave(filename = paste0("QC_", obj_name, ".pdf"),
         plot = p4b, 
         device = "pdf", 
         path = "/blue/rbacher/j.leary/repos/scLANE_Analysis/Figures/QC_Plots/", 
         width = 13,
         height = 9, 
         units = "in", 
         dpi = "retina")
  print(p4a)
  # cleanup 
  sink(tempfile())
  rm(p0, p1, p2a, p2b, p3a, p3b, p3c, p4a, plot_table); gc(full = TRUE)
  sink()
})
rm(obj_list)
```

# Session Info 

```{r}
sessioninfo::session_info()
```
